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This book constitutes the refereed proceedings of the First International Workshop on Connectomics in NeuroImaging, CNI 2017, held in conjunction with MICCAI 2017 in Quebec City, Canada, in September 2017. The 19 full papers presented were carefully reviewed and selected from 26 submissions. The papers deal with new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies as well as in various neuroimaging applications.
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• International Workshop on Fetal and Infant Image Analysis, FIFI 2017:
• Template-Free Estimation of Intracranial Volume: A Preterm Birth Animal Model Study / Juan Eugenio Iglesias, Sebastiano Ferraris, Marc Modat, Willy Gsell, Jan Deprest, Johannes L. van der Merwe et al.
• Assessing Reorganisation of Functional Connectivity in the Infant Brain / Roxane Licandro, Karl-Heinz Nenning, Ernst Schwartz, Kathrin Kollndorfer, Lisa Bartha-Doering, Hesheng Liu et al.
• Fetal Skull Segmentation in 3D Ultrasound via Structured Geodesic Random Forest / Juan J. Cerrolaza, Ozan Oktay, Alberto Gomez, Jacqueline Matthew, Caroline Knight, Bernhard Kainz et al.
• Fast Registration of 3D Fetal Ultrasound Images Using Learned Corresponding Salient Points / Alberto Gomez, Kanwal Bhatia, Sarjana Tharin, James Housden, Nicolas Toussaint, Julia A. Schnabel
• Automatic Segmentation of the Intracranial Volume in Fetal MR Images / N. Khalili, P. Moeskops, N. H. P. Claessens, S. Scherpenzeel, E. Turk, R. de Heus et al.
• Abdomen Segmentation in 3D Fetal Ultrasound Using CNN-powered Deformable Models / Alexander Schmidt-Richberg, Tom Brosch, Nicole Schadewaldt, Tobias Klinder, Angelo Cavallaro, Ibtisam Salim et al.
• Multi-organ Detection in 3D Fetal Ultrasound with Machine Learning / Caroline Raynaud, Cybèle Ciofolo-Veit, Thierry Lefèvre, Roberto Ardon, Angelo Cavallaro, Ibtisam Salim et al.
• Robust Regression of Brain Maturation from 3D Fetal Neurosonography Using CRNs / Ana I. L. Namburete, Weidi Xie, J. Alison Noble
• 4th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017:
• Segmentation of Retinal Blood Vessels Using Dictionary Learning Techniques / Taibou Birgui Sekou, Moncef Hidane, Julien Olivier, Hubert Cardot
• Detecting Early Choroidal Changes Using Piecewise Rigid Image Registration and Eye-Shape Adherent Regularization / Tiziano Ronchetti, Peter Maloca, Christoph Jud, Christoph Meier, Selim Orgül, Hendrik P. N. Scholl et al.
• Patch-Based Deep Convolutional Neural Network for Corneal Ulcer Area Segmentation / Qichao Sun, Lijie Deng, Jianwei Liu, Haixiang Huang, Jin Yuan, Xiaoying Tang
• Model-Driven 3-D Regularisation for Robust Segmentation of the Refractive Corneal Surfaces in Spiral OCT Scans / Joerg Wagner, Simon Pezold, Philippe C. Cattin
• Automatic Retinal Layer Segmentation Based on Live Wire for Central Serous Retinopathy / Dehui Xiang, Geng Chen, Fei Shi, Weifang Zhu, Xinjian Chen
• Retinal Image Quality Classification Using Fine-Tuned CNN / Jing Sun, Cheng Wan, Jun Cheng, Fengli Yu, Jiang Liu
• Optic Disc Detection via Deep Learning in Fundus Images / Peiyuan Xu, Cheng Wan, Jun Cheng, Di Niu, Jiang Liu
• 3D Choroid Neovascularization Growth Prediction with Combined Hyperelastic Biomechanical Model and Reaction-Diffusion Model / Chang Zuo, Fei Shi, Weifang Zhu, Haoyu Chen, Xinjian Chen
• Retinal Biomarker Discovery for Dementia in an Elderly Diabetic Population / Ahmed E. Fetit, Siyamalan Manivannan, Sarah McGrory, Lucia Ballerini, Alexander Doney, Thomas J. MacGillivray et al.
• Non-rigid Registration of Retinal OCT Images Using Conditional Correlation Ratio / Xueying Du, Lun Gong, Fei Shi, Xinjian Chen, Xiaodong Yang, Jian Zheng
• Joint Optic Disc and Cup Segmentation Using Fully Convolutional and Adversarial Networks / Sharath M. Shankaranarayana, Keerthi Ram, Kaushik Mitra, Mohanasankar Sivaprakasam
• Automated Segmentation of the Choroid in EDI-OCT Images with Retinal Pathology Using Convolution Neural Networks / Min Chen, Jiancong Wang, Ipek Oguz, Brian L. VanderBeek, James C. Gee
• Spatiotemporal Analysis of Structural Changes of the Lamina Cribrosa / Charly Girot, Hiroshi Ishikawa, James Fishbaugh, Gadi Wollstein, Joel Schuman, Guido Gerig
• Fast Blur Detection and Parametric Deconvolution of Retinal Fundus Images / Bryan M. Williams, Baidaa Al-Bander, Harry Pratt, Samuel Lawman, Yitian Zhao, Yalin Zheng et al.
• Towards Topological Correct Segmentation of Macular OCT from Cascaded FCNs / Yufan He, Aaron Carass, Yeyi Yun, Can Zhao, Bruno M. Jedynak, Sharon D. Solomon et al.
• Boosted Exudate Segmentation in Retinal Images Using Residual Nets / Samaneh Abbasi-Sureshjani, Behdad Dashtbozorg, Bart M. ter Haar Romeny, François Fleuret
• Development of Clinically Based Corneal Nerves Tortuosity Indexes / Fabio Scarpa, Alfredo Ruggeri
• A Comparative Study Towards the Establishment of an Automatic Retinal Vessel Width Measurement Technique / Fan Huang, Behdad Dashtbozorg, Alexander Ka Shing Yeung, Jiong Zhang, Tos T. J. M. Berendschot, Bart M. ter Haar Romeny
• Automatic Detection of Folds and Wrinkles Due to Swelling of the Optic Disc / Jason Agne, Jui-Kai Wang, Randy H. Kardon, Mona K. Garvin
• Representation Learning for Retinal Vasculature Embeddings / Luca Giancardo, Kirk Roberts, Zhongming Zhao.

This book constitutes the refereed joint proceedings of the International Workshop on Fetal and Infant Image Analysis, FIFI 2017, and the 6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 8 full papers presented at FIFI 2017 and the 20 full papers presented at OMIA 2017 were carefully reviewed and selected. The FIFI papers feature research on advanced image analysis approaches focused on the analysis of growth and development in the fetal, infant and paediatric period. The OMIA papers cover various topics in the field of ophthalmic image analysis.
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• Novel imaging and analysis methods for myocardial tissue characterization and remodeling.- Advanced cardiac image analysis tools for diagnostic and interventions.- Electrophysiology: mapping and biophysical modeling.- Biomechanics and flow: modeling and tissue property measurements.
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This book constitutes the refereed proceedings of the 9th International Conference on Functional Imaging and Modeling of the Heart, held in Toronto, ON, Canada, in June 2017. The 48 revised full papers were carefully reviewed and selected from 63 submissions. The focus of the papers is on following topics: novel imaging and analysis methods for myocardial tissue characterization and remodeling; advanced cardiac image analysis tools for diagnostic and interventions; electrophysiology: mapping and biophysical modeling; biomechanics and flow: modeling and tissue property measurements.
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• Intro
• Workshop Editors
• Preface GRAIL 2017
• Organization
• Preface MFCA 2017
• Organization
• Preface MICGen 2017
• Organization
• Contents
• First International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017
• Classifying Phenotypes Based on the Community Structure of Human Brain Networks
• 1 Introduction
• 2 Similarity of Brain Network Community Structures
• 2.1 Detecting Communities in Structural Brain Networks
• 2.2 Measuring Distance Between Community Structures
• 3 Classifying Connectomes Based on their Community Structure

• 4 Experiments: Network-Based Alzheimer's Disease Classification
• 4.1 Data and Network Construction
• 4.2 Experimental Setup
• 4.3 Results and Discussion
• 5 Conclusions
• References
• Autism Spectrum Disorder Diagnosis Using Sparse Graph Embedding of Morphological Brain Networks
• 1 Introduction
• 2 Proposed Sparse Graph Embedding of High-Order Morphological Brain Networks for Autism Classification
• 3 Results and Discussion
• 4 Conclusion
• References
• Topology of Surface Displacement Shape Feature in Subcortical Structures
• 1 Introduction
• 2 Methods
• 2.1 Shape Feature

• 2.2 Shape Topology
• 2.3 Persistent Homology
• 2.4 Experiments
• 2.5 Imaging and Demographics
• 3 Results
• 4 Discussion and Conclusion
• References
• Graph Geodesics to Find Progressively Similar Skin Lesion Images
• 1 Introduction
• 2 Methods
• 3 Results
• 4 Conclusions
• References
• Uncertainty Estimation in Vascular Networks
• 1 Introduction
• 2 Background
• 3 Uncertainty Estimation by Means of Sampling
• 3.1 Perturbation Sampler
• 3.2 Gibbs Sampler
• 4 Experiments and Results
• 5 Conclusion
• References

• Extraction of Airways with Probabilistic State-Space Models and Bayesian Smoothing
• 1 Introduction
• 2 Method
• 2.1 Tracking Individual Branches
• 2.2 Process and Measurement Models
• 2.3 Bayesian Smoothing
• 2.4 Tree as a Collection of Branches
• 2.5 Application to Airways
• 3 Experiments and Results
• 3.1 Data
• 3.2 Error Measure, Initial Parameters and Tuning
• 3.3 Results
• 4 Discussion and Conclusions
• References
• Detection and Localization of Landmarks in the Lower Extremities Using an Automatically Learned Conditional Random Field
• 1 Introduction
• 2 Related Work

• 3 Methods
• 3.1 Landmark Localization Using Regression Tree Ensembles
• 3.2 CRF with Pool of Potential Functions and ``Missing'' Label
• 3.3 Learning of Parameters and Removing Potentials
• 4 Results
• 5 Discussion and Conclusions
• References
• 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017
• Bridge Simulation and Metric Estimation on Landmark Manifolds
• 1 Introduction
• 2 Landmarks Manifolds and Stochastic Landmark Dynamics
• 2.1 Brownian Motion
• 2.2 Large Deformation Stochastics
• 3 Brownian Bridge Simulation
• 3.1 Bridge Sampling

This book constitutes the refereed joint proceedings of the First International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017, the 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017, and the Third International Workshop on Imaging Genetics, MICGen 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 7 full papers presented at GRAIL 2017, the 10 full papers presented at MFCA 2017, and the 5 full papers presented at MICGen 2017 were carefully reviewed and selected. The GRAIL papers cover a wide range of graph based medical image analysis methods and applications, including probabilistic graphical models, neuroimaging using graph representations, machine learning for diagnosis prediction, and shape modeling. The MFCA papers deal with theoretical developments in non-linear image and surface registration in the context of computational anatomy. The MICGen papers cover topics in the field of medical genetics, computational biology and medical imaging.
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• Analysis on manifolds.- Shape analysis.- Disease diagnosis/progression.- Brain networks an connectivity.- Diffusion imaging.- Quantitative imaging.- Imaging genomics.- Image registration.- Segmentation.- General image analysis. <.
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This book constitutes the proceedings of the 25th International Conference on Information Processing in Medical Imaging, IPMI 2017, held at the Appalachian State University, Boon, NC, USA, in June 2017. The 53 full papers presented in this volume were carefully reviewed and selected from 147 submissions. They were organized in topical sections named: analysis on manifolds; shape analysis; disease diagnosis/progression; brain networks an connectivity; diffusion imaging; quantitative imaging; imaging genomics; image registration; segmentation; general image analysis.
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• Introduction.- Medical foundations.- Elements of fuzzy set theory.- Cardinalities of interval-valued fuzzy sets and their applications in decision making with imperfect information.- OvaExpert System.- Summary.
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This book discusses computer-supported medical diagnosis with a particular focus on ovarian tumor diagnosis - since ovarian cancer is difficult to diagnose and has high mortality rates, especially in Central and Eastern Europe. It presents the theoretical foundations (both medical and mathematical) of the intelligent OvaExpert system, which supports decision-making in tumor diagnosis. OvaExpert was created primarily to help gynecologists predict the malignancy of ovarian tumors by applying the existing diagnostic models and using modern methods of computational intelligence that accommodate imprecise and imperfect medical data, both of which are common features of everyday medical practice. The book presents novel methods based on interval-valued fuzzy sets and the theory of their cardinalities.
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• From Large to Small Organ Segmentation in CT Using Regional Context.- Motion Corruption Detection in Breast DCE-MRI.- Detection and Localization of Drosophila Egg Chambers in Microscopy Images.- Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-specific Coronary Calcium Scoring.- Atlas of Classifiers for Brain MRI Segmentation.- Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis.- Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer's Disease.- Multi-Factorial Age Estimation from Skeletal and Dental MRI Volumes.- Automatic Classification of Proximal Femur Fractures Based on Attention Models.- Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation.- Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble.- STAR: Spatio-Temporal Architecture for Super-Resolution in Low-Dose CT Perfusion.- Classification of Alzheimer's Disease by Cascaded Convolutional Neural Networks Using PET Images.- Finding Dense Supervoxel Correspondence of Cone-Beam Computed Tomography Images.- Multi-Scale Volumetric ConvNet with Nested Residual Connections for Segmentation of Anterior Cranial Base.- Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-Status Dementia Diagnosis.- 3D Convolutional Neural Networks with Graph Refinement for Airway Segmentation Using Incomplete Data Labels.- Efficient Groupwise Registration for Brain MRI by Fast Initialization.- Sparse Multi-View Task-centralized Learning for ASD Diagnosis.- Inter-Subject Similarity Guided Brain Network Modelling for MCI Diagnosis.- Scalable and Fault Tolerant Platform for Distributed Learning on Private Medical Data.- Triple-Crossing 2.5D Convolutional Neural Network for Detecting Neuronal Arbours in 3D Microscopic Images.- Longitudinally-Consistent Parcellation of Infant Population Cortical Surfaces Based on Functional Connectivity.- Gradient Boosted Trees for Corrective Learning.- Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis.- A Point Says a Lot: An Interactive Segmentation Method for MR Prostate via One-Point Labeling.- Collage CNN for Renal Cell Carcinoma Detection from CT.- Aggregating Deep Convolutional Features for Melanoma Recognition in Dermoscopy Images.- Localizing Cardiac Structures in Fetal Heart Ultrasound Video.- Deformable Registration Through Learning of Context-Specific Metric Aggregation.- Segmentation of Craniomaxillofacial Bony Structures from MRI with a 3D Deep-learning Based Cascade Framework.- 3D U-net with Multi-Level Deep Supervision: Fully Automatic Segmentation of Proximal Femur in 3D MR Images.- Indecisive Trees for Classification and Prediction of Knee Osteoarthritis.- Whole Brain Segmentation and Labeling from CT using synthetic MR Images.- Structural Connectivity Guided Sparse Effective Connectivity for MCI Identification.- Fusion of High-order and Low-order Effective Connectivity Networks for MCI Classification.- Novel Effective Connectivity Network Inference for MCI Identification.- Reconstruction of Thin-Slice Medical Images Using Generative Adversarial Network.- Neural Network Convolution (NNC) for Converting Ultra-Low-Dose to "Virtual" High-Dose CT Images.- Deep-Fext: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction.- Product Space Decompositions for Continuous Representations of Brain Connectivity.- Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks.- Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging.- Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks.
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This book constitutes the refereed proceedings of the 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017, held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 44 full papers presented in this volume were carefully reviewed and selected from 63 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging.
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• Fifth International Workshop on Computational Methods for Molecular Imaging, CMMI 2017:
• 3D Lymphoma Segmentation in PET/CT Images Based on Fully Connected CRFs / Yuntao Yu, Pierre Decazes, Isabelle Gardin, Pierre Vera, Su Ruan
• Individual Analysis of Molecular Brain Imaging Data Through Automatic Identification of Abnormality Patterns / Ninon Burgos, Jorge Samper-González, Anne Bertrand, Marie-Odile Habert, Sébastien Ourselin, Stanley Durrleman et al.
• W-Net for Whole-Body Bone Lesion Detection on 68 Ga-Pentixafor PET/CT Imaging of Multiple Myeloma Patients / Lina Xu, Giles Tetteh, Mona Mustafa, Jana Lipkova, Yu Zhao, Marie Bieth et al.
• 3D Alpha Matting Based Co-segmentation of Tumors on PET-CT Images / Zisha Zhong, Yusung Kim, John Buatti, Xiaodong Wu
• Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs) / Lei Bi, Jinman Kim, Ashnil Kumar, Dagan Feng, Michael Fulham
• Second International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2017:
• Dynamic Respiratory Motion Estimation Using Patch-Based Kernel-PCA Priors for Lung Cancer Radiotherapy / Tiancheng He, Ramiro Pino, Bin Teh, Stephen Wong, Zhong Xue
• Mass Transportation for Deformable Image Registration with Application to Lung CT / Bartłomiej W. Papież, Sir Michael Brady, Julia A. Schnabel
• Motion-Robust Spatially Constrained Parameter Estimation in Renal Diffusion-Weighted MRI by 3D Motion Tracking and Correction of Sequential Slices / Sila Kurugol, Bahram Marami, Onur Afacan, Simon K. Warfield, Ali Gholipour
• Semi-automatic Cardiac and Respiratory Gated MRI for Cardiac Assessment During Exercise / Bram Ruijsink, Esther Puyol-Antón, Muhammad Usman, Joshua van Amerom, Phuoc Duong, Mari Nieves Velasco Forte et al.
• CoronARe: A Coronary Artery Reconstruction Challenge / Serkan Çimen, Mathias Unberath, Alejandro Frangi, Andreas Maier
• Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks / Yipeng Hu, Eli Gibson, Li-Lin Lee, Weidi Xie, Dean C. Barratt, Tom Vercauteren et al.
• Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging / Steven McDonagh, Benjamin Hou, Amir Alansary, Ozan Oktay, Konstantinos Kamnitsas, Mary Rutherford et al.
• Reconstruction of 3D Cardiac MR Images from 2D Slices Using Directional Total Variation / Nicolas Basty, Darryl McClymont, Irvin Teh, Jürgen E. Schneider, Vicente Grau
• An Efficient Multi-resolution Reconstruction Scheme with Motion Compensation for 5D Free-Breathing Whole-Heart MRI / Rosa-María Menchón-Lara, Javier Royuela-del-Val, Alejandro Godino-Moya, Lucilio Cordero-Grande, Federico Simmross-Wattenberg, Marcos Martín-Fernández et al.
• First International Stroke Workshop on Imaging and Treatment Challenges, SWITCH 2017:
• Automated Ventricular System Segmentation in CT Images of Deformed Brains Due to Ischemic and Subarachnoid Hemorrhagic Stroke / E. Ferdian, A. M. Boers, L. F. Beenen, B. M. Cornelissen, I. G. Jansen, K. M. Treurniet et al.
• Towards Automatic Collateral Circulation Score Evaluation in Ischemic Stroke Using Image Decompositions and Support Vector Machines / Yiming Xiao, Ali Alamer, Vladimir Fonov, Benjamin W. Y. Lo, Donatella Tampieri, D. Louis Collins et al.
• The Effect of Non-contrast CT Slice Thickness on Thrombus Density and Perviousness Assessment / M. L. Tolhuisen, J. Enthoven, E. M. M. Santos, W. J. Niessen, L. F. M. Beenen, D. W. J. Dippel et al.
• Quantitative Collateral Grading on CT Angiography in Patients with Acute Ischemic Stroke / Anna M. M. Boers, Renan Sales Barros, Ivo G. H. Jansen, Cornelis H. Slump, Diederik W. J. Dippel, Aad van der Lugt et al.

This book constitutes the refereed joint proceedings of the International Workshop on Computational Methods for Molecular Imaging, CMMI 2017, the International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2017, and the International Stroke Workshop: Imaging and Treatment Challenges, SWITCH 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 5 full papers presented at FIFI 2017, the 9 full papers presented at RAMBO 2017, and the 4 full papers presented at SWITCH 2017 were carefully reviewed and selected. The CMMI papers cover various areas from image synthesis to data analysis and from clinical diagnosis to therapy individualization, using molecular imaging modalities PET, SPECT, PET/CT, SPECT/CT, and PET/MR. The RAMBO papers present research from both academia and industry, They are organized into the categories "registration and tracking" and "image reconstruction and information retrieval" while application areas include cardiac, pulmonal, abdominal, fetal, and renal imaging. The SWITCH papers focus on CT(A)-based quantitative imaging biomarkers for stroke.
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• Multi-atlas segmentation.- Segmentation.- Alzheimer's disease.- Reconstruction, denoising, super-resolution.- Tumor, lesion.- Classification, retrival.
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This book constitutes the refereed proceedings of the Third International Workshop on Patch-Based Techniques in Medical Images, Patch-MI 2017, which was held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 18 regular papers presented in this volume were carefully reviewed and selected from 26 submissions. The papers are organized in topical sections on multi-atlas segmentation; segmentation; Alzheimer's disease; reconstruction, denoising, super-resolution; tumor, lesion; and classification, retrival.
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• Purpose and Scope of the Monograph.- Introduction.- Image Acquisition.- Image Pre-processing.- Image Processing.- Examples of Tailoring the Algorithm.
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The book presents automatic and reproducible methods for the analysis of medical infrared images. All methods highlighted here have been practically implemented in Matlab, and the source code is presented and discussed in detail. Further, all methods have been verified with medical specialists, making the book an ideal resource for all IT specialists, bioengineers and physicians who wish to broaden their knowledge of tailored methods for medical infrared image analysis and processing.
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• Processing of Hyperspectral Medical Images: Applications in Dermatology Using Matlab (R).- Foreword.- Preface.- Acknowledgments.- List of selected symbols.- Chapte
• r1: Introduction.- Chapte
• r2: Image acquisition.- Chapte
• r3: Image pre-processing.- Chapte
• r4: Image processing.- Chapte
• r5: Classification.- Chapte
• r6: Sensitivity to parameter changes.- Chapte
• r7: Conclusions.- Appendix.- References.- Summary.
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This book presents new methods of analyzing and processing hyperspectral medical images, which can be used in diagnostics, for example for dermatological images. The algorithms proposed are fully automatic and the results obtained are fully reproducible. Their operation was tested on a set of several thousands of hyperspectral images and they were implemented in Matlab. The presented source code can be used without licensing restrictions. This is a valuable resource for computer scientists, bioengineers, doctoral students, and dermatologists interested in contemporary analysis methods.
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• Invited Talks.- Dice overlap measures for objects of unknown number: Application to lesion segmentation.- Lesion Detection, Segmentation and Prediction in Multiple Sclerosis Clinical Trials.- Brain Lesion Image Analysis.- Automated Segmentation of Multiple Sclerosis Lesions using Multi-Dimensional Gated Recurrent Units.- Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation.- MARCEL (inter-Modality Ane Registration with CorELation ratio): An Application for Brain Shift Correction in Ultrasound-Guided Brain Tumor Resection.- Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks.- Overall Survival Time Prediction for High Grade Gliomas based on Sparse Representation Framework.- Traumatic Brain Lesion Quantication based on Mean Diusivity Changes.- Pairwise, Ordinal Outlier Detection of Traumatic Brain Injuries.- Sub-Acute & Chronic Ischemic Stroke Lesion MRI Segmentation.- Brain Tumor Segmentation Using an Adversarial Network.- Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma.- Brain Tumor Image Segmentation.- Deep Learning based Multimodal Brain Tumor Diagnosis.- Multimodal Brain Tumor Segmentation using Ensemble of Forest Method.- Pooling-free fully convolutional networks with dense skip connections for semantic segmentation, with application to brain tumor segmentation.- Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks.- 3D Brain Tumor Segmentation through Integrating Multiple 2D FCNNs.- MRI Brain Tumor Segmentation and Patient Survival Prediction using Random Forests and Fully Convolutional Networks.- Automatic Segmentation and Overall Survival Prediction in Gliomas using Fully Convolutional Neural Network and Texture Analysis.- Multimodal Brain Tumor Segmentation Using 3D Convolutional Networks.- A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor.- Dilated Convolutions for Brain Tumor Segmentation in MRI Scans.- Residual Encoder and Convolutional Decoder Neural Network for Glioma Segmentation.- TPCNN: Two-phase Patch-based Convolutional Neural Network for Automatic Brain Tumor Segmentation and Survival Prediction.- Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge.- Multi-modal PixelNet for Brain Tumor Segmentation.- Brain Tumor Segmentation using Dense Fully Convolutional Neural Network.- Brain Tumor Segmentation in MRI Scans using Deeply-Supervised Neural Networks.- Brain Tumor Segmentation and Parsing on MRIs using Multiresolution Neural Networks.- Brain Tumor Segmentation using Deep Fully Convolutional Neural Networks.- Glioblastoma and Survival Prediction.- MRI Augmentation via Elastic Registration for Brain Lesions Segmentation.- Cascaded V-Net using ROI masks for brain tumor segmentation.- Brain Tumor Segmentation using a 3D FCN with Multi-Scale Loss.- Brain tumor segmentation using a multi-path CNN based method.- 3D Deep Neural Network-Based Brain Tumor Segmentation Using Multimodality Magnetic Resonance Sequences.- Automated Brain Tumor Segmentation on Magnetic Resonance Images (MRIs) and Patient Overall Survival Prediction using Support Vector Machines.- Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation.- Tumor segmentation from multimodal MRI using random forest with superpixel and tensor based feature extraction.- Towards Uncertainty-assisted Brain Tumor Segmentation and Survival Prediction.- Ischemic Stroke Lesion Image Segmentation.- WMH Segmentation Challenge: a Texture-based Classication Approach.- White Matter Hyperintensities Segmentation In a Few Seconds Using Fully Convolutional Network and Transfer Learning.
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This book constitutes revised selected papers from the Third International MICCAI Brainlesion Workshop, BrainLes 2017, as well as the International Multimodal Brain Tumor Segmentation, BraTS, and White Matter Hyperintensities, WMH, segmentation challenges, which were held jointly at the Medical Image computing for Computer Assisted Intervention Conference, MICCAI, in Quebec City, Canada, in September 2017. The 40 papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; and ischemic stroke lesion image segmentation.
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• Towards Ultra-high Resolution 3D Reconstruction of a Whole Rat Brain from 3D-PLI Data.- FOD-based Registration for Susceptibility Distortion Correction in Connectome Imaging.- GIFE: Efficient and Robust Group-wise Isometric Fiber Embedding.- Multi-Modal Brain Tensor Factorization: Preliminary Results with AD Patients.- Intact Connectional Morphometricity Learning Using Multi-View Morphological Brain Networks with Application to Autism Spectrum Disorder.- Neonatal Morphometric Similarity Networks Predict Atypical Brain Development Associated with Preterm Birth.- Heritability Estimation of Reliable Connectomic Features.- Topological Data Analysis of Functional MRI Connectivity in Time and Space Domains.- Riemannian Regression and Classification Models of Brain Networks Applied to Autism.- Defining Patient Specific Functional Parcellations in Lesional Cohorts via Markov Random Fields.- Data-Specific Feature Selection Method Identification for Most Reproducible Connectomic Feature Discovery Fingerprinting Brain States.- Towards Effective Functional Connectome Fingerprinting.- Connectivity-Driven Brain Parcellation via Consensus Clustering.- GRAND: Unbiased Connectome Atlas of Brain Network by Groupwise Graph Shrinkage and Network Diffusion.- Structural Subnetwork Evolution Across the Lifespan: Rich-club, Feeder, Seeder.
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This book constitutes the refereed proceedings of the Second International Workshop on Connectomics in NeuroImaging, CNI 2018, held in conjunction with MICCAI 2018 in Granada, Spain, in September 2018. The 15 full papers presented were carefully reviewed and selected from 20 submissions. The papers deal with new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies as well as in various neuroimaging applications.
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• Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity.- A Graph Representation and Similarity Measure for Brain Networks with Nodal Features.- Hierarchical Bayesian Networks for Modeling Inter-Class Dependencies: Application to Semi-Supervised Cell Segmentation.- Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion.- BrainParcel: A Brain Parcellation Algorithm for Cognitive State Classification.- Modeling Brain Networks with Artificial Neural Networks.- A Bayesian Disease Progression Model for Clinical Trajectories.- Multi-modal brain connectivity study using deep collaborative learning.- Towards Subject and Diagnostic Identifiability in the Alzheimer's Disease Spectrum based on Functional Connectomes.- Predicting Conversion of Mild Cognitive Impairments to Alzheimer's Disease and Exploring Impact of Neuroimaging.- Cross-Diagnostic Prediction of Dimensional Psychiatric Phenotypes in Anorexia Nervosa and Body Dysmorphic Disorder Using Multimodal Neuroimaging and Psychometric Data.
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This book constitutes the refereed joint proceedings of the Second International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018 and the First International Workshop on Integrating Medical Imaging and Non-Imaging Modalities, Beyond MIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 6 full papers presented at GRAIL 2018 and the 5 full papers presented at BeYond MIC 2018 were carefully reviewed and selected. The GRAIL papers cover a wide range of develop graph-based models for the analysis of biomedical images and encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts. The Beyond MIC papers cover topics of novel methods with significant imaging and non-imaging components, addressing practical applications and new datasets.
(source: Nielsen Book Data)

• Application to Body Composition Measurements on CT scans.- 3D Pulmonary Artery Segmentation from CTA Scans using Deep Learning with Realistic Data Augmentation.- Automatic Airway Segmentation in chest CT using Convolutional Neural Networks.- Detecting Out-of-phase Ventilation Using 4DCT to Improve Radiation Therapy for Lung Cancer.- XeMRI to CT Lung Image Registration Enhanced with Personalized 4DCT-derived Motion Model.- Rigid Lens
• Locally Rigid Approximations of Deformable Registration for Change Assessment in Thorax-Abdomen CT Follow-Up Scan.- Diffeomorphic Lung Registration using Deep CNNs and Reinforced Learning.- Transfer learning approach to predict biopsy-confirmed malignancy of lung nodules from imaging data: a pilot study.- Convolutional Neural Network Based COPD and Emphysema Classifications Are Predictive of Lung Cancer Diagnosis.- Towards an automatic lung cancer screening system in low dose computed tomography.- Automatic classification of centrilobular emphysema on.

This book constitutes the refereed joint proceedings of the Third International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2018, the Fourth International Workshop on Breast Image Analysis, BIA 2018, and the First International Workshop on Thoracic Image Analysis, TIA 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 5 full papers (out of 10 submissions) presented at RAMBO, the 9 full papers (out of 18 submissions) presented at BIA, and the 20 full papers (out of 21 submissions) presented at TIA were carefully reviewed and selected. The RAMBO papers cover aspects of medical imaging where motion plays a role in the image formation or analysis. The BIA papers deal with topics such as computer-aided detection and diagnosis of breast cancer, quantitative analysis of breast imaging modalities, and large scale breast image screening and analysis. The TIA papers cover aspects of image analysis research for lung and cardiac diseases including segmentation, registration, quantification, modeling of the image acquisition process, visualization, validation, statistical modeling, biophysical lung modeling (computational anatomy), deep learning and novel applications.

• Intro
• Preface
• Organization
• Contents
• Developing Novel Weighted Correlation Kernels for Convolutional Neural Networks to Extract Hierarchical Functional Connectivities from fMRI for Disease Diagnosis
• 1 Introduction
• 2 Method
• 2.1 Subjects and Image Preprocessing
• 2.2 Proposed Weighted Correlation Kernel
• 2.3 Architecture of the Proposed Wc-Kernel Based CNN
• 3 Experiments
• 4 Conclusion
• References
• Robust Contextual Bandit via the Capped-2 Norm for Mobile Health Intervention
• 1 Introduction
• 2 Preliminaries
• 3 Robust Contextual Bandit with Capped-2 Norm

• 3.1 Algorithm for the Critic Updating
• 3.2 Algorithm for the Actor Updating
• 4 Experiments
• 4.1 Datasets
• 4.2 Experiments Settings
• 4.3 Results and Discussion
• 5 Conclusions and Future Directions
• References
• Dynamic Multi-scale CNN Forest Learning for Automatic Cervical Cancer Segmentation
• Abstract
• 1 Introduction
• 2 Proposed Cluster-Based Dynamic Multi-scale Dynamic Forest
• 2.1 Root Node CNN Architecture
• 2.2 Cascaded CNNs
• 2.3 Proposed CNN-Based Dynamic Multi-scale Tree (DMT)
• 2.4 Proposed CK+1DMF Learning Framework
• 3 Results and Discussion
• 4 Conclusion

• 3.2 Training Parameters
• 3.3 Evaluation
• 4 Results
• 4.1 FROC Analysis
• 4.2 Reconstructed Images
• 5 Conclusion and Discussion
• References
• CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement
• 1 Introduction
• 2 Methods
• 2.1 CT Image Enhancement
• 2.2 Lesion Segmentation
• 3 Experimental Results and Analyses
• 4 Conclusions
• References
• Deep Learning Based Inter-modality Image Registration Supervised by Intra-modality Similarity
• Abstract
• 1 Introduction
• 2 Method

• 2.1 Loss Function Based on Intra-modality Similarity
• 2.2 Inter-modality Registration Network
• 2.3 Spatial Transformation Layer
• 3 Experimental Results
• 3.1 Registration Results
• 4 Conclusion
• References
• Regional Abnormality Representation Learning in Structural MRI for AD/MCI Diagnosis
• 1 Introduction
• 2 Materials and Preprocessing
• 3 Proposed Method
• 3.1 Regional Abnormality Representation
• 3.2 Brain-Wise Feature Extraction and Classifier Learning
• 4 Experimental Settings and Results
• 4.1 Experimental Settings
• 4.2 Results and Discussion
• 5 Conclusion

This book constitutes the proceedings of the 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018, held in conjunction with MICCAI 2018 in Granada, Spain, in September 2018.The 45 papers presented in this volume were carefully reviewed and selected from 82 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.
(source: Nielsen Book Data)

• Part I: Image Quality and Artefacts
• Image Reconstruction Methods
• Machine Learning in Medical Imaging
• Statistical Analysis for Medical Imaging
• Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications
• Histology Applications
• Microscopy Applications
• Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications
• Lung Imaging Applications
• Breast Imaging Applications
• Other Abdominal Applications. Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging
• Diffusion Weighted Imaging
• Functional MRI
• Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging
• Brain Segmentation Methods. Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery
• Surgical Planning, Simulation and Work Flow Analysis
• Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications
• Multi-Organ Segmentation
• Abdominal Segmentation Methods
• Cardiac Segmentation Methods
• Chest, Lung and Spine Segmentation
• Other Segmentation Applications. .
• (source: Nielsen Book Data)

The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018. The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: Part I: Image Quality and Artefacts; Image Reconstruction Methods; Machine Learning in Medical Imaging; Statistical Analysis for Medical Imaging; Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications; Histology Applications; Microscopy Applications; Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications; Lung Imaging Applications; Breast Imaging Applications; Other Abdominal Applications. Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging; Diffusion Weighted Imaging; Functional MRI; Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging; Brain Segmentation Methods. Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery; Surgical Planning, Simulation and Work Flow Analysis; Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications; Multi-Organ Segmentation; Abdominal Segmentation Methods; Cardiac Segmentation Methods; Chest, Lung and Spine Segmentation; Other Segmentation Applications. .
(source: Nielsen Book Data)

• Part I: Image Quality and Artefacts-- Image Reconstruction Methods-- Machine Learning in Medical Imaging-- Statistical Analysis for Medical Imaging-- Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications-- Histology Applications-- Microscopy Applications-- Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications-- Lung Imaging Applications-- Breast Imaging Applications-- Other Abdominal Applications.
• Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging-- Diffusion Weighted Imaging-- Functional MRI-- Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging-- Brain Segmentation Methods.
• Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery-- Surgical Planning, Simulation and Work Flow Analysis-- Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications-- Multi-Organ Segmentation-- Abdominal Segmentation Methods-- Cardiac Segmentation Methods-- Chest, Lung and Spine Segmentation-- Other Segmentation Applications. .
• (source: Nielsen Book Data)

The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018. The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: Part I: Image Quality and Artefacts; Image Reconstruction Methods; Machine Learning in Medical Imaging; Statistical Analysis for Medical Imaging; Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications; Histology Applications; Microscopy Applications; Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications; Lung Imaging Applications; Breast Imaging Applications; Other Abdominal Applications. Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging; Diffusion Weighted Imaging; Functional MRI; Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging; Brain Segmentation Methods. Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery; Surgical Planning, Simulation and Work Flow Analysis; Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications; Multi-Organ Segmentation; Abdominal Segmentation Methods; Cardiac Segmentation Methods; Chest, Lung and Spine Segmentation; Other Segmentation Applications. .
(source: Nielsen Book Data)

• Special LNCS price list
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• Infotext
• The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018.
• The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: Part I: Image Quality and Artefacts-- Image Reconstruction Methods-- Machine Learning in Medical Imaging-- Statistical Analysis for Medical Imaging-- Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications-- Histology Applications-- Microscopy Applications-- Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications-- Lung Imaging Applications-- Breast Imaging Applications-- Other Abdominal Applications.
• Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging-- Diffusion Weighted Imaging-- Functional MRI-- Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging-- Brain Segmentation Methods.
• Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery-- Surgical Planning, Simulation and Work Flow Analysis-- Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications-- Multi-Organ Segmentation-- Abdominal Segmentation Methods-- Cardiac Segmentation Methods-- Chest, Lung and Spine Segmentation-- Other Segmentation Applications.
• SEO
• The MICCAI 2018 proceedings volumes present papers focusing on Reconstruction and Image Quality, Machine Learning and Statistical Analysis, Registration and Image Guidance, Optical and Histology Applications, Chest and Abdominal Applications, fMRI and Diffusion Imaging.
• Short TOC
• Part I: Image Quality and Artefacts-- Image Reconstruction Methods-- Machine Learning in Medical Imaging-- Statistical Analysis for Medical Imaging-- Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications-- Histology Applications-- Microscopy Applications-- Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications-- Lung Imaging Applications-- Breast Imaging Applications-- Other Abdominal Applications.
• Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging-- Diffusion Weighted Imaging-- Functional MRI-- Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging-- Brain Segmentation Methods.
• Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery-- Surgical Planning, Simulation and Work Flow Analysis-- Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications-- Multi-Organ Segmentation-- Abdominal Segmentation Methods-- Cardiac Segmentation Methods-- Chest, Lung and Spine Segmentation-- Other Segmentation Applications.
• .
• (source: Nielsen Book Data)

The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018. The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: Part I: Image Quality and Artefacts; Image Reconstruction Methods; Machine Learning in Medical Imaging; Statistical Analysis for Medical Imaging; Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications; Histology Applications; Microscopy Applications; Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications; Lung Imaging Applications; Breast Imaging Applications; Other Abdominal Applications. Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging; Diffusion Weighted Imaging; Functional MRI; Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging; Brain Segmentation Methods. Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery; Surgical Planning, Simulation and Work Flow Analysis; Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications; Multi-Organ Segmentation; Abdominal Segmentation Methods; Cardiac Segmentation Methods; Chest, Lung and Spine Segmentation; Other Segmentation Applications. .
(source: Nielsen Book Data)

• Part I: Image Quality and Artefacts-- Image Reconstruction Methods-- Machine Learning in Medical Imaging-- Statistical Analysis for Medical Imaging-- Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications-- Histology Applications-- Microscopy Applications-- Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications-- Lung Imaging Applications-- Breast Imaging Applications-- Other Abdominal Applications.
• Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging-- Diffusion Weighted Imaging-- Functional MRI-- Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging-- Brain Segmentation Methods.
• Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery-- Surgical Planning, Simulation and Work Flow Analysis-- Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications-- Multi-Organ Segmentation-- Abdominal Segmentation Methods-- Cardiac Segmentation Methods-- Chest, Lung and Spine Segmentation-- Other Segmentation Applications. .
• (source: Nielsen Book Data)

The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018. The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: Part I: Image Quality and Artefacts; Image Reconstruction Methods; Machine Learning in Medical Imaging; Statistical Analysis for Medical Imaging; Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications; Histology Applications; Microscopy Applications; Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications; Lung Imaging Applications; Breast Imaging Applications; Other Abdominal Applications. Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging; Diffusion Weighted Imaging; Functional MRI; Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging; Brain Segmentation Methods. Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery; Surgical Planning, Simulation and Work Flow Analysis; Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications; Multi-Organ Segmentation; Abdominal Segmentation Methods; Cardiac Segmentation Methods; Chest, Lung and Spine Segmentation; Other Segmentation Applications. .
(source: Nielsen Book Data)

• Image Denoising.- Image Registration and Matching.- Image Classification and Detection.- Brain Image Analysis.- Retinal Image Analysis.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 4th International Workshop on Patch-Based Techniques in Medical Images, Patch-MI 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 15 full papers presented were carefully reviewed and selected from 17 submissions. The papers are organized in the following topical sections: Image Denoising Image Registration and Matching, Image Classification and Detection, Brain Image Analysis, and Retinal Image Analysis.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Shape Applications/Validation/Software
• Deformetrica 4: An Open-Source Software for Statistical Shape Analysis
• 1 Introduction
• 2 Theoretical Background
• 2.1 Control-Points-Based LDDMM: Constructing Diffeomorphisms
• 2.2 Diffeomorphic Action on Shapes: Deforming Meshes or Images
• 2.3 Shape Attachments: Evaluting Deformation Residuals
• 2.4 A Glimpse at Optimization
• 3 Performances
• 4 Deformetrica Applications
• 4.1 Atlas and Registration
• 4.2 Bayesian Atlas
• 4.3 Geodesic Regression
• 4.4 Parallel Transport in Shape Analysis

• 5 Conclusion
• References
• On the Evaluation and Validation of Off-the-Shelf Statistical Shape Modeling Tools: A Clinical Application
• 1 Introduction
• 2 Methods
• 2.1 Statistical Shape Models
• 2.2 SSM Tools
• 2.3 Evaluation Methodology
• 2.4 Validation Methodology
• 3 Results
• 3.1 Experimental Setup
• 3.2 Shape Models Evaluation
• 3.3 Shape Models Validation
• 4 Conclusion
• References
• Characterizing Anatomical Variability and Alzheimer's Disease Related Cortical Thinning in the Medial Temporal Lobe Using Graph-Based Groupwise Registration and Point Set Geodesic Shooting

• Abstract
• 1 Introduction
• 2 Materials and Method
• 2.1 Dataset
• 2.2 Construction of Statistical Models of Anatomical Variants of the PRC
• 2.2.1 Template Construction Using Graph-Based Groupwise Registration
• 2.2.2 Quantifying Shape Variability Using Pointset Geodesic Shooting
• 2.3 Fitting the Templates to a New Target Image
• 3 Experiments and Results
• 3.1 Statistical Shape Models
• 3.2 AD-Related Cortical Thinning
• 3.3 Effect of AD on MTL Shape
• 4 Conclusion
• Acknowledgements
• References
• Interpretable Spiculation Quantification for Lung Cancer Screening

• 1 Introduction
• 2 Method
• 2.1 Conformal Mappings and Area Distortion
• 2.2 Spiculation Quantification Pipeline
• 2.3 Spiculation Score
• 2.4 Spiculation Classification and Malignancy Prediction
• 3 Results
• 3.1 Spiculation Classification
• 3.2 Malignancy Prediction
• 4 Conclusion and Future Work
• References
• Shape and Facet Analyses of Alveolar Airspaces of the Lung
• 1 Introduction
• 2 Methods
• 2.1 Sample Preparation, Data Acquisition and Reconstruction
• 2.2 Segmentation, Partition Creation and Processing
• 2.3 Quantities per Alveoli and Histograms

• 2.4 Facet Analysis of Alveoli
• 2.5 Shape Analysis of Alveoli
• 2.6 Processing Dependencies, Source Code and Reproduction
• 3 Results
• 3.1 Morphometric Data of Individual Alveoli
• 3.2 Angle Distribution Between Interalveolar Septa
• 3.3 Distribution of the Number of Neighboring Alveoli
• 3.4 Shape of Individual Alveolar Airspaces
• 4 Discussion
• 5 Conclusion
• A Catalogue
• B Video
• References
• SlicerSALT: Shape AnaLysis Toolbox
• 1 Introduction
• 2 Available Extensions
• 2.1 Home
• 2.2 Data Importer
• 2.3 SPHARM-PDM

This book constitutes the proceedings of the Workshop on Shape in Medical Imaging, ShapeMI 2018, held in conjunction with the 21st International Conference on Medical Image Computing, MICCAI 2018, in Granada, Spain, in September 2018. The 26 full papers and 2 short papers presented were carefully reviewed and selected for inclusion in this volume. The papers discuss novel approaches and applications in shape and geometry processing and their use in research and clinical studies and explore novel, cutting-edge theoretical methods and their usefulness for medical applications, e.g., from the fields of geometric learning or spectral shape analysis. .
(source: Nielsen Book Data)

• Robust Photoacoustic Beamforming using Dense Convolutional Neural Networks.- A Training Tool for Ultrasound-guided Central Line Insertion with Webcam-based Position Tracking.- GLUENet: Ultrasound Elastography Using Convolutional Neural Network.- CUST: CNN for Ultrasound thermal image reconstruction using Sparse Time-of-flight information.- Quality Assessment of Fetal Head Ultrasound Images Based on Faster R-CNN.- Recent Advances in Point-of-Care Ultrasound using the ImFusion Suite for Real-Time Image Analysis.- Markerless Inside-Out Tracking for 3D Ultrasound Compounding.- Ultrasound-based Detection of Lung Abnormalities using Single Shot Detection Convolutional Neural Networks.- Quantitative Echocardiography: Real-time Quality Estimation and View Classification Implemented on a Mobile Android Device.- Single-Element Needle-Based Ultrasound Imaging of the Spine: An In Vivo Feasibility Study.- A novel interventional guidance framework for transseptal puncture in left atrial interventions.- Holographic visualisation and interaction of fused CT, PET and MRI volumetric medical imaging data using dedicated remote GPGPU ray casting
• Mr. Silva and Patient Zero: a medical social network and data visualization information system.- Fully Convolutional Network-based Eyeball Segmentation from Sparse Annotation for Eye Surgery Simulation Model.- Resolve Intraoperative Brain Shift as Imitation Game.- Non-linear approach for MRI to intra-operative US registration using structural skeleton.- Brain-shift correction with image-based registration and landmark accuracy evaluation.- Deformable MRI-ultrasound Registration Using 3D Convolutional Neural Network.- Intra-operative Ultrasound to MRI Fusion with a Public Multimodal Discrete Registration Tool.- Deformable MRI-Ultrasound Registration via Attribute Matching and Mutual-saliency Weighting for Image guided Neurosurgery.- Registration of MRI and iUS data to compensate brain shift using a symmetric block-matching based approach.- Intra-operative Brain Shift Correction with Weighted Locally Linear Correlations of 3DUS and MRI.- Survival modeling of pancreatic cancer with radiology using convolutional neural networks.- Pancreatic Cancer Survival Prediction Using CT Scans and Clinical Variables.
• (source: Nielsen Book Data)

This book constitutes the refereed joint proceedings of the International Workshop on Point-of-Care Ultrasound, POCUS 2018, the International Workshop on Bio-Imaging and Visualization for Patient-Customized Simulations, BIVPCS 2017, the International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2018, and the International Workshop on Computational Precision Medicine, CPM 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018.The 10 full papers presented at POCUS 2018, the 4 full papers presented at BIVPCS 2018, the 8 full papers presented at CuRIOUS 2018, and the 2 full papers presented at CPM 2018 were carefully reviewed and selected. The papers feature research from complementary fields such as ultrasound image systems applications as well as signal and image processing, mechanics, computational vision, mathematics, physics, informatics, computer graphics, bio-medical-practice, psychology and industry. They discuss intra-operative ultrasound-guided brain tumor resection as well as pancreatic cancer survival prediction.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Regular Papers
• Multiview Machine Learning Using an Atlas of Cardiac Cycle Motion
• 1 Introduction
• 2 Materials
• 3 Methods
• 3.1 Motion Atlas Formation
• 3.2 Multiview Classification
• 4 Experiments and Results
• 5 Discussion
• References
• Joint Myocardial Registration and Segmentation of Cardiac BOLD MRI
• 1 Introduction
• 2 Background
• 3 Methods
• 3.1 Dictionary Learning Based Image Segmentation
• 3.2 Graph-Based Joint Optimization
• 3.3 Dictionary Update
• 4 Experimental Results

• 4.1 Data Preparation and Implementation Details
• 4.2 Visual Evaluation
• 4.3 Quantitative Comparison
• 4.4 CAP Dataset
• 5 Conclusion
• References
• Transfer Learning for the Fully Automatic Segmentation of Left Ventricle Myocardium in Porcine Cardiac Cine MR Images
• Abstract
• 1 Introduction
• 2 Method
• 2.1 Data Description
• 2.2 Image Preprocessing
• 2.3 CNN Architecture and Training Setup
• 2.4 Transfer Learning
• 3 Experiments and Results
• 4 Conclusion and Discussions
• References
• Left Atrial Appendage Neck Modeling for Closure Surgery
• 1 Introduction

• 2 LAA Segmentation
• 3 LAA Neck Modeling
• 3.1 Auto-Detection of the Ostium of the LAA
• 3.2 Establishment of the Standard Coordinate System Based on the Ostium Plane
• 3.3 Auto-Building of Circumscribed Cylindrical Model of LAA Neck
• 4 Experiments and Results
• 4.1 Dataset
• 4.2 Ground Truth
• 4.3 Evaluation
• 5 Conclusion
• References
• Detection of Substances in the Left Atrial Appendage by Spatiotemporal Motion Analysis Based on 4D-CT
• 1 Introduction
• 2 Method
• 2.1 Extraction of Optical Flow Fields of Adjacent Phase
• 2.2 The Tracking of Key Voxels in Whole Cardiac Cycle

• 2.3 Hierarchical Clustering of All Trajectory Curves
• 2.4 Time-Frequency Analysis of the Track Curve of Critical Lumps
• to Realize the Stress and Strain Detection of Lumps
• 3 Experiment and Discussion
• 3.1 Dataset
• 3.2 Evaluation and Results
• 4 Conclusion
• References
• Estimation of Healthy and Fibrotic Tissue Distributions in DE-CMR Incorporating CINE-CMR in an EM Algorithm
• 1 Introduction
• 2 Methods
• 3 Experimental Results
• 4 Conclusions
• References
• Multilevel Non-parametric Groupwise Registration in Cardiac MRI: Application to Explanted Porcine Hearts

• 1 Introduction
• 2 Methods
• 2.1 Data Acquisition
• 2.2 Pairwise Registration of the Anatomical MR Images
• 3 Groupwise Registration
• 4 Results
• 5 Future Work and Conclusions
• References
• ACDC Challenge
• GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation
• 1 Introduction
• 2 Our Method
• 2.1 Shape Prior
• 2.2 Loss
• 2.3 Proposed Network
• 3 Experimental Setup and Results
• 3.1 Dataset, Evaluation Criteria, and Other Methods
• 3.2 Experimental Results
• 4 Conclusion
• References

This book constitutes the thoroughly refereed post-workshop proceedings of the 8th International Workshop on Statistical Atlases and Computational Models of the Heart: ACDC and MMWHS Challenges 2017, held in conjunction with MICCAI 2017, in Quebec, Canada, in September 2017. The 27 revised full workshop papers were carefully reviewed and selected from 35 submissions. The papers cover a wide range of topics computational imaging and modelling of the heart, as well as statistical cardiac atlases. The topics of the workshop included: cardiac imaging and image processing, atlas construction, statistical modelling of cardiac function across different patient populations, cardiac computational physiology, model customization, atlas based functional analysis, ontological schemata for data and results, integrated functional and structural analyses, as well as the pre-clinical and clinical applicability of these methods. Besides regular contributing papers, additional efforts of STACOM workshop were also focused on two challenges: ACDC and MM-WHS.
(source: Nielsen Book Data)

• Intro
• Additional Workshop Editors
• MLCN 2018 Preface
• DLF 2018 Preface
• iMIMIC 2018 Preface
• Organization
• Contents
• First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018
• Alzheimer's Disease Modelling and Staging Through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes
• 1 Introduction
• 2 Method
• 3 Results
• 3.1 Benchmark on Synthetic Data
• 3.2 Application on Real Data
• 4 Conclusion
• References

• Multi-channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease
• 1 Introduction
• 2 Method
• 2.1 Multi-channel Variational Inference
• 2.2 Gaussian Linear Case
• 3 Experiments
• 3.1 Experiments on Linearly Generated Synthetic Datasets
• 3.2 Application to Clinical and Medical Imaging Data in AD
• 4 Discussion and Conclusion
• References
• Visualizing Convolutional Networks for MRI-Based Diagnosis of Alzheimer's Disease
• 1 Introduction
• 2 Related Work
• 2.1 Alzheimer Classification
• 2.2 Visualization Methods

• 3 Methods
• 3.1 Data
• 3.2 Model
• 3.3 Visualization Methods
• 4 Results
• 4.1 Classification
• 4.2 Relevant Brain Areas
• 4.3 Differences Between Visualization Methods
• 5 Conclusion
• References
• Finding Effective Ways to (Machine) Learn fMRI-Based Classifiers from Multi-site Data
• 1 Introduction
• 1.1 Multi-site Data and Batch Effects
• 2 Machine Learning and Functional Connectivity Graphs
• 3 Batch Effects Correction Techniques
• 3.1 Adding Site as Covariate
• 3.2 Z-Score Normalization
• 3.3 Whitening
• 3.4 Solving Linear Transformations
• 4 Experiments and Results

• 4.1 Dataset
• 4.2 Experiments and Results
• 5 Discussion
• References
• First International Workshop on Deep Learning Fails Workshop, DLF 2018
• Towards Robust CT-Ultrasound Registration Using Deep Learning Methods
• 1 Introduction
• 2 Methods
• 3 Data
• 3.1 Clinical Data
• 3.2 Training Data
• 4 Experiments
• 4.1 Mono-Modal
• 4.2 Multi-modal (Simulated)
• 4.3 Inaccurate Ground Truth
• 4.4 CT-US
• 5 Discussion and Conclusion
• References
• To Learn or Not to Learn Features for Deformable Registration?
• 1 Introduction
• 2 Method
• 2.1 Discrete Optimization

• 2.2 Deep Learning Framework
• 3 Experiments and Results
• 3.1 Datasets Description
• 3.2 Evaluation Metric
• 3.3 Implementation Detail
• 3.4 Feature Learning Experiments and Results
• 4 Conclusions
• References
• Evaluation of Strategies for PET Motion Correction
• Manifold Learning vs. Deep Learning
• 1 Introduction
• 2 Methods
• 2.1 Network Architecture
• 2.2 Training Details
• 3 Experiments
• 3.1 Synthetic Dataset
• 3.2 Comparison Method: Data-Driven Gating
• 3.3 Assessment of Corrected Volume Quality
• 4 Discussion and Conclusions
• References

This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis. .
(source: Nielsen Book Data)

• Temporal-Adaptive Graph Convolutional Network for Automated Identification of Major Depressive Disorder with Resting-State fMRI.- Error Attention Interactive Segmentation of Medical Images through Matting and Fusion.- A Novel fMRI Representation Learning Framework with GAN.- Semi-supervised Segmentation with Self-Training Based on Quality Estimation and Refinement.- 3D Segmentation Networks for Excessive Numbers of Classes: Distinct Bone Segmentation in Upper Bodies.- Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network.- Self-Recursive Contextual Network for Unsupervised 3D Medical Image Registration.- Automated Tumor Proportion Scoring for Assessment of PD-L1 Expression Based on Multi-Stage Ensemble Strategy.- Uncertainty Quantification in Medical Image Segmentation with Normalizing Flows.- Out-of-Distribution Detection for Skin Lesion Images with Deep Isolation Forest.- A 3D+2D CNN Approach Incorporating Boundary Loss for Stroke Lesion Segmentation.- Linking Adolescent Brain MRI to Obesity via Deep Multi-cue Regression Network.- Robust Multiple Sclerosis Lesion Inpainting with Edge Prior.- Segmentation to Label: Automatic Coronary Artery Labeling from Mask Parcellation.- GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes.- Anatomy-Aware Cardiac Motion Estimation.- Division and Fusion: Rethink Convolutional Kernels for 3D Medical Image Segmentation.- LDGAN: Longitudinal-Diagnostic Generative Adversarial Network for Disease Progression Prediction with Missing Structural MRI.- Unsupervised MRI Homogenization: Application to Pediatric Anterior Visual Pathway Segmentation.- Boundary-aware Network for Kidney Tumor Segmentation.- O-Net: An Overall Convolutional Network for Segmentation Tasks.- Label-Driven Brain Deformable Registration Using Structural Similarity and Nonoverlap Constraints.- EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis.- Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation.- Joint Appearance-Feature Domain Adaptation: Application to QSM Segmentation Transfer.- Exploring Functional Difference between Gyri and Sulci via Region-Specific 1D Convolutional Neural Networks.- Detection of Ischemic Infarct Core in Non-Contrast Computed Tomography.- Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers.- Extended Capture Range of Rigid 2D/3D Registration by Estimating Riemannian Pose Gradients.- Structural Connectivity Enriched Functional Brain Network using Simplex Regression with GraphNet.- Constructing High-Order Dynamic Functional Connectivity Networks from Resting-State fMRI for Brain Dementia Identification.- Multi-tasking Siamese Networks for Breast Mass Detection using Dual-view Mammogram Matching.- 3D Volume Reconstruction from Single Lateral X-ray Image via Cross-Modal Discrete Embedding Transition.- Cleft Volume Estimation and Maxilla Completion Using Cascaded Deep Neural Networks.- A Deep Network for Joint Registration and Reconstruction of Images with Pathologies.- Learning Conditional Deformable Shape Templates for Brain Anatomy .- Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity.- Unsupervised Learning for Spherical Surface Registration.- Anatomy-guided Convolutional Neural Network for Motion Correction in Fetal Brain MRI.- Gyral Growth Patterns of Macaque Brains Revealed by Scattered Orthogonal Nonnegative Matrix Factorization.- Inhomogeneity Correction in Magnetic Resonance Images Using Deep Image Priors.- Hierarchical and Robust Pathology Image Reading for High-Throughput Cervical Abnormality Screening .- Importance Driven Continual Learning for Segmentation Across Domains.- RDCNet: Instance segmentation with a minimalist recurrent residual network.- Automatic Segmentation of Achilles Tendon Tissues using Deep Convolutional Neural Network.- An End to End System for Measuring Axon Growth.- Interwound Structural and Functional Difference Between Preterm and Term Infant Brains Revealed by Multi-view CCA.- Graph Convolutional Network Based Point Cloud for Head and Neck Vessel Labeling
• .- Unsupervised Learning-based Nonrigid Registration of High Resolution Histology Images.- Additive Angular Margin for Few Shot Learning to Classify Clinical Endoscopy Images.- Extracting and Leveraging Nodule Features with Lung Inpainting for Local Feature Augmentation.- Gambling Adversarial Nets for Hard Sample Mining and Structured Prediction: Application in Ultrasound Thyroid Nodule Segmentation.- Mammographic Image Conversion between Source and Target Acquisition Systems using CGAN.- An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation.- Neural Architecture Search for Microscopy Cell Segmentation.- Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using Vascular Pattern Detection.- Predicting Catheter Ablation Outcomes from Heart Rhythm Time-series: Less Is More.- AdaBoosted Deep Ensembles: Getting Maximum Performance Out of Small Training Datasets.- Cross-Task Representation Learning for Anatomical Landmark Detection.- Cycle Ynet: Semi-supervised Tracking of 3D Anatomical Landmarks.- Learning Hierarchical Semantic Correspondence and Gland Instance Segmentation.- Open-Set Recognition for Skin Lesions using Dermoscopic Images.- End-to-End Coordinate Regression Model with Attention-Guided Mechanism for Landmark Localization in 3D Medical Images.- Enhanced MRI Reconstruction Network using Neural Architecture Search.- Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets.- Noise-aware Standard-dose PET Reconstruction Using General and Adaptive Robust Loss.- Semi-supervised Transfer Learning for Infant Cerebellum Tissue Segmentation .- Informative Feature-guided Siamese Network for Early Diagnosis of ASD.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 68 papers presented in this volume were carefully reviewed and selected from 101 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.
(source: Nielsen Book Data)

• Image Reconstruction.- Improving Amide Proton Transfer-weighted MRI Reconstruction using T2-weighted Images.- Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations.- Active MR k-space Sampling with Reinforcement Learning.- Fast Correction of Eddy-Current and Susceptibility-Induced Distortions Using Rotation-Invariant Contrasts.- Joint reconstruction and bias field correction for undersampled MR imaging.- Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping.- End-to-End Variational Networks for Accelerated MRI Reconstruction.- 3d-SMRnet: Achieving a new quality of MPI system matrix recovery by deep learning.- MRI Image Reconstruction via Learning Optimization Using Neural ODEs.- An evolutionary framework for microstructure-sensitive generalized diffusion gradient waveforms.- Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images using a GAN.- T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions.- Learned Proximal Networks for Quantitative Susceptibility Mapping.- Learning A Gradient Guidance for Spatially Isotropic MRI Super-Resolution Reconstruction.- Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography.- Acceleration of High-resolution 3D MR Fingerprinting via a Graph Convolutional Network.- Deep Attentive Wasserstein Generative Adversarial Network for MRI Reconstruction with Recurrent Context-Awareness.- Learning MRI $k$-Space Subsampling Pattern using Progressive Weight Pruning.- Model-driven Deep Attention Network for Ultra-fast Compressive Sensing MRI Guided by Cross-contrast MR Image.- Simultaneous Estimation of X-ray Back-Scatter and Forward-Scatter using Multi-Task Learning.- Prediction and Diagnosis.- MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response.- M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients.- Automatic Detection of Free Intra-Abdominal Air in Computed Tomography.- Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Deep Learning with Integrative Imaging, Molecular and Demographic Data.- Geodesically Smoothed Tensor Features for Pulmonary Hypertension Prognosis using the Heart and Surrounding Tissues.- Ovarian Cancer Prediction in Proteomic Data Using Stacked Asymmetric Convolution.- DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Dynamic Contrast-Enhanced CT Imaging.- Holistic Analysis of Abdominal CT for Predicting the Grade of Dysplasia of Pancreatic Lesions.- Feature-enhanced Graph Networks for Genetic Mutational Prediction Using Histopathological Images in Colon cancer.- Spatial-And-Context aware (SpACe) "virtual biopsy'' radiogenomic maps to target tumor mutational status on structural MRI.- CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis.- Preoperative prediction of lymph node metastasis from clinical DCE MRI of the primary breast tumor using a 4D CNN.- Learning Differential Diagnosis of Skin Conditions with Co-occurrence Supervision using Graph Convolutional Networks.- Cross-Domain Methods and Reconstruction.- Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation.- Dynamic memory to alleviate catastrophic forgetting in continuous learning settings.- Unlearning Scanner Bias for MRI Harmonisation.- Cross-Domain Image Translation by Shared Latent Gaussian Mixture Model.- Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy.- X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph.- Domain Adaptation for Ultrasound Beamforming.- CDF-Net: Cross-Domain Fusion Network for accelerated MRI reconstruction.- Domain Adaptation.- Improve Unseen Domain Generalization via Enhanced Local Color Transformation and Augmentation.- Transport-based Joint Distribution Alignment for Multi-site Autism Spectrum Disorder Diagnosis using Resting-state fMRI.- Automatic and interpretable model for periodontitis diagnosis in panoramic radiographs.- Residual-CycleGAN based Camera Adaptation for Robust Diabetic Retinopathy Screening.- Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains.- Automatic Plane Adjustment of Orthopedic Intraoperative Flat Panel Detector CT-Volumes.- Unsupervised Graph Domain Adaptation for Neurodevelopmental Disorders Diagnosis.- JBFnet - Low Dose CT Denoising by Trainable Joint Bilateral Filtering.- MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint.- Machine Learning Applications.- Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment.- Domain-specific loss design for unsupervised physical training: A new approach to modeling medical ML solutions.- Multiatlas Calibration of Biophysical Brain Tumor Growth Models with Mass Effect.- Chest X-ray Report Generation through Fine-Grained Label Learning.- Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time.- A Deep Bayesian Video Analysis Framework: Towards a More Robust Estimation of Ejection Fraction.- Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications.- Large-scale inference of liver fat with neural networks on UK Biobank body MRI.- BUNET: Blind Medical Image Segmentation Based on Secure UNET.- Temporal-consistent Segmentation of Echocardiography with Co-learning from Appearance and Shape.- Decision Support for Intoxication Prediction Using Graph Convolutional Networks.- Latent-Graph Learning for Disease Prediction.- Generative Adversarial Networks.- BR-GAN: Bilateral Residual Generating Adversarial Network for Mammogram Classification.- Cycle Structure and Illumination Constrained GAN for Medical Image Enhancement.- Generating Dual-Energy Subtraction Soft-Tissue Images from Chest Radiographs via Bone Edge-Guided GAN.- GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI.- Brain MR to PET Synthesis via Bidirectional Generative Adversarial Network.- AGAN: An Anatomy Corrector Conditional Generative Adversarial Network.- SteGANomaly: Inhibiting CycleGAN Steganography for Unsupervised Anomaly Detection in Brain MRI.- Flow-based Deformation Guidance for Unpaired Multi-Contrast MRI Image-to-Image Translation.- Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields.- Spatial-Intensity Transform GANs for High Fidelity Medical Image-to-Image Translation.- Graded Image Generation Using Stratified CycleGAN.- Prediction of Plantar Shear Stress Distribution by Conditional GAN with Attention Mechanism.
• (source: Nielsen Book Data)

The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic.The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography.
(source: Nielsen Book Data)

• Methods.- Composition of Transformations in the Registration of Sets of Points or Oriented Points.- Uncertainty reduction in contour-based 3D/2D registration of bone surfaces.- Learning Shape Priors from Pieces.- Bi-invariant Two-Sample Tests in Lie Groups for Shape Analysis.- Learning.- Uncertain-DeepSSM: From Images to Probabilistic Shape Models.- D-Net: Siamese based Network for Arbitrarily Oriented Volume Alignment.- A Method for Semantic Knee Bone and Cartilage Segmentation with Deep 3D Shape Fitting Using Data From the Osteoarthritis Initiative.- Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes.- Applications.- Combined Estimation of Shape and Pose for Statistical Analysis of Articulating Joints.- Learning a statistical full spine model from partial observations.- Morphology-based individual vertebrae classification.- Patient Specific Classification of Dental Root Canal and Crown Shape.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the International Workshop on Shape in Medical Imaging, ShapeMI 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assistend Intervention, MICCAI 2020, in October 2020. The conference was planned to take place in Lima, Peru, but changed to a virtual format due to the COVID-19 pandemic. The 12 full papers included in this volume were carefully reviewed and selected from 18 submissions. They were organized in topical sections named: methods; learning; and applications.
(source: Nielsen Book Data)

• Chapter 1. An Integrated Design of Fuzzy C-Means and NCA based Multi-Properties Features Reduction for Brain Tumor Recognition.-
• Chapter 2. Hybrid Image Processing based Examination of 2D Brain MRI Slices to Detect Brain Tumour/Stroke Section - A Study.-
• Chapter 3. Edge Enhancing Coherence Diffusion Filter for Level Set Segmentation and Asymmetry Analysis using Curvelets in Breast Thermograms.-
• Chapter 4. Lung Cancer Diagnosis Based on Image Fusion and prediction using CT and PET image.-
• Chapter 5. Segmentation and Validation of Infrared Breast Images using Weighted Level Set and Phase Congruency Edge Map Framework.-
• Chapter 6. Analysis of Material Profile for Polymer Based Mechanical Microgripper for Thin Plate Holding.-
• Chapter 7. Design and Testing of Elbow Actuated Wearable Robotic Arm for Muscular Disorders.-
• Chapter 8. A Comprehensive Study of Image Fusion Techniques and Their Applications.-
• Chapter 9. Multilevel Mammogram Image Analysis for Identifying Outliers, Misclassification using Machine Learning.-
• Chapter 10. A Review on Automatic Detection of Retinal Lesions in Fundus Images for Diabetic Retinopathy.-
• Chapter 11. Medical Image Watermarking: A Review on Wavelet Based Methods.-
• Chapter 12. EEG Signal Extraction Analysis Techniques.-
• Chapter 13. Classification of sEMG Signal based Arm Action using Convolutional Neural Network.-
• Chapter 14. An Automated Approach for the Identification of TB Images Enhanced by Non-uniform Illumination Correction.
• (source: Nielsen Book Data)

This book comprehensively reviews the various automated and semi-automated signal and image processing techniques, as well as deep-learning-based image analysis techniques, used in healthcare diagnostics. It highlights a range of data pre-processing methods used in signal processing for effective data mining in remote healthcare, and discusses pre-processing using filter techniques, noise removal, and contrast-enhanced methods for improving image quality. The book discusses the status quo of artificial intelligence in medical applications, as well as its future. Further, it offers a glimpse of feature extraction methods for reducing dimensionality and extracting discriminatory information hidden in biomedical signals. Given its scope, the book is intended for academics, researchers and practitioners interested in the latest real-world technological innovations.
(source: Nielsen Book Data)

• Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis.- 3D Brain MRI GAN-based Synthesis Conditioned on Partial Volume Maps.- Synthesizing Realistic Brain MR Images With Noise Control.- Simulated Diffusion Weighted Images Based on Model-Predicted Tumor Growth.- Blind MRI Brain Lesion Inpainting Using Deep Learning.- High-Quality Interpolation of Breast DCE-MRI Using Learned Transformations.- A Method for Tumor Treating Fields Fast Estimation.- Heterogeneous Virtual Population of Simulated CMR Images for Improving the Generalization of Cardiac Segmentation Algorithms.- DyeFreeNet: Deep Virtual Contrast CT Synthesis.- A Gaussian Process Model Based Generative Framework for Data Augmentation of Multi-modal 3D Image Volumes.- Frequency-selective Learning for CT to MR Synthesis.- Uncertainty-aware Multi-resolution Whole-body MR to CT Synthesis.- UltraGAN: Ultrasound Enhancement Through Adversarial Generation.- Improving Endoscopic Decision Support Systems by Translating Between Imaging Modalities.- An Unsupervised Adversarial Learning Approach to Fundus Fluorescein Angiography Image Synthesis for Leakage Detection.- Towards Automatic Embryo Staging in 3D+t Microscopy Images Using Convolutional Neural Networks and PointNets.- Train Small, Generate Big: Synthesis of Colorectal Cancer Histology Images.- Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis.- Auditory Nerve Fiber Health Estimation Using Patient Specific Cochlear Implant Stimulation Models.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 5th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The 19 full papers presented were carefully reviewed and selected from 27 submissions. The contributions span the following broad categories in alignment with the initial call-for-papers: methods based on generative models or adversarial learning for MRI/CT/PET/microscopy image synthesis, and several applications of image synthesis and simulation for data augmentation, image enhancement or segmentation.
(source: Nielsen Book Data)

• Image Segmentation.- Image Registration, Reconstruction and Enhancement.- Radiomics, Predictive Models, and Quantitative Imaging Biomarkers.- Ocular Imaging Analysis.- Biomedical Simulation and Modelling.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 24th Conference on Medical Image Understanding and Analysis, MIUA 2020, held in July 2020. Due to COVID-19 pandemic the conference was held virtually. The 29 full papers and 5 short papers presented were carefully reviewed and selected from 70 submissions. They were organized according to following topical sections: image segmentation; image registration, reconstruction and enhancement; radiomics, predictive models, and quantitative imaging biomarkers; ocular imaging analysis; biomedical simulation and modelling.
(source: Nielsen Book Data)

• Brain Lesion Image Analysis
• Brain Tumor Image Segmentation
• Combined MRI and Pathology Brain Tumor Classification
• Tools Allowing Clinical Translation of Image Computing Algorithms.

The two-volume set LNCS 11992 and 11993 constitutes the thoroughly refereed proceedings of the 5th International MICCAI Brainlesion Workshop, BrainLes 2019, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge, as well as the tutorial session on Tools Allowing Clinical Translation of Image Computing Algorithms (TACTICAL). These were held jointly at the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI, in Shenzhen, China, in October 2019. The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (12 selected papers from 32 submissions); brain tumor image segmentation (58 selected papers from 102 submissions); combined MRI and pathology brain tumor classification (4 selected papers from 5 submissions); tools allowing clinical translation of image computing algorithms (2 selected papers from 3 submissions.).

• Brain Lesion Image Analysis
• Brain Tumor Image Segmentation
• Combined MRI and Pathology Brain Tumor Classification
• Tools Allowing Clinical Translation of Image Computing Algorithms.

The two-volume set LNCS 11992 and 11993 constitutes the thoroughly refereed proceedings of the 5th International MICCAI Brainlesion Workshop, BrainLes 2019, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge, as well as the tutorial session on Tools Allowing Clinical Translation of Image Computing Algorithms (TACTICAL). These were held jointly at the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI, in Shenzhen, China, in October 2019. The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (12 selected papers from 32 submissions); brain tumor image segmentation (58 selected papers from 102 submissions); combined MRI and pathology brain tumor classification (4 selected papers from 5 submissions); tools allowing clinical translation of image computing algorithms (2 selected papers from 3 submissions.).

• Oncology and Tumour Imaging
• Lesion, Wound and Ulcer Analysis
• Biostatistics
• Fetal Imaging
• Enhancement and Reconstruction
• Diagnosis, Classication and Treatment
• Vessel and Nerve Analysis
• Image Registration
• Image Segmentation
• Ophthalmic Imaging
• Posters.

This book constitutes the refereed proceedings of the 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019, held in Liverpool, UK, in July 2019. The 43 full papers presented were carefully reviewed and selected from 70 submissions. There were organized in topical sections named: oncology and tumour imaging; lesion, wound and ulcer analysis; biostatistics; fetal imaging; enhancement and reconstruction; diagnosis, classification and treatment; vessel and nerve analysis; image registration; image segmentation; ophthalmic imaging; and posters.

• Brain Lesion.- Brain Tumor Segmentation (BRATS).- Ischemic Stroke Lesion Image Segmentation (ISLES), Mild Traumatic Brain Injury Outcome Prediction (mTOP).
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Brain Lesion, as well as the challenges on Brain Tumor Segmentation (BRATS), Ischemic Stroke Lesion Image Segmentation (ISLES), and the Mild Traumatic Brain Injury Outcome Prediction (mTOP), held in Athens, October 17, 2016, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016. The 26 papers presented in this volume were carefully reviewed. They present the latest advances in segmentation, disease prognosis and other applications to the clinical context.
(source: Nielsen Book Data)

• Spectral methods
• Longitudinal methods
• Shape methods.

This book constitutes the refereed post-conference proceedings of the First International Workshop on Spectral and Shape Analysis in Medical Imaging, SeSAMI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 10 submitted full papers presented in this volume were carefully reviewed. The papers reflect the following topics: spectral methods; longitudinal methods; and shape methods.

This book constitutes the refereed proceedings of the 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 38 full papers presented in this volume were carefully reviewed and selected from 60 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging.

• Brain analysis
• Brain analysis
• connectivity
• Brain analysis
• cortical morphology
• Alzheimer disease
• Surgical guidance and tracking
• Computer aided interventions
• Ultrasound image analysis
• cancer image analysis.

The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers have been organized in the following topical sections: Part I: brain analysis; brain analysis - connectivity; brain analysis - cortical morphology; Alzheimer disease; surgical guidance and tracking; computer aided interventions; ultrasound image analysis; cancer image analysis; Part II: machine learning and feature selection; deep learning in medical imaging; applications of machine learning; segmentation; cell image analysis; Part III: registration and deformation estimation; shape modeling; cardiac and vascular image analysis; image reconstruction; and MR image analysis.

• Machine learning and feature selection.- Deep learning in medical imaging.- Applications of machine learning.- Segmentation.- Cell image analysis.
• (source: Nielsen Book Data)

The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers have been organized in the following topical sections: Part I: brain analysis, brain analysis - connectivity; brain analysis - cortical morphology; Alzheimer disease; surgical guidance and tracking; computer aided interventions; ultrasound image analysis; cancer image analysis; Part II: machine learning and feature selection; deep learning in medical imaging; applications of machine learning; segmentation; cell image analysis; Part III: registration and deformation estimation; shape modeling; cardiac and vascular image analysis; image reconstruction; and MR image analysis.
(source: Nielsen Book Data)

• Registration and deformation estimation
• Shape modeling
• Cardiac and vascular image analysis
• Image reconstruction
• MR image analysis.

The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers have been organized in the following topical sections: Part I: brain analysis, brain analysis - connectivity; brain analysis - cortical morphology; Alzheimer disease; surgical guidance and tracking; computer aided interventions; ultrasound image analysis; cancer image analysis; Part II: machine learning and feature selection; deep learning in medical imaging; applications of machine learning; segmentation; cell image analysis; Part III: registration and deformation estimation; shape modeling; cardiac and vascular image analysis; image reconstruction; and MR image analysis.

• Automatic Segmentation of Hippocampus for Longitudinal Infant Brain MR Image Sequence by Spatial-Temporal Hypergraph Learning
• Construction of Neonatal Diffusion Atlases via Spatio-Angular Consistency
• Selective Labeling: identifying representative sub-volumes for interactive segmentation
• Robust and Accurate Appearance Models based on Joint Dictionary Learning: Data from the Osteoarthritis Initiative
• Consistent multi-atlas hippocampus segmentation for longitudinal MR brain images with temporal sparse representation
• Sparse-Based Morphometry: Principle and Application to Alzheimer's Disease
• Multi-Atlas Based Segmentation of Brainstem Nuclei from MR Images by Deep Hyper-Graph Learning
• Patch-Based Discrete Registration of Clinical Brain Images
• Non-local MRI Library-based Super-resolution: Application to Hippocampus Subfield Segmentation
• Patch-based DTI grading: Application to Alzheimer's disease classification
• Hierarchical Multi-Atlas Segmentation using Label-Specific Embeddings, Target-Specific Templates and Patch Refinement
• HIST: HyperIntensity Segmentation Tool
• Supervoxel-Based Hierarchical Markov Random Field Framework for Multi-Atlas Segmentation
• CapAIBL: Automated reporting of cortical PET quantification without need of MRI on brain surface using a patch-based method
• High resolution hippocampus subfield segmentation using multispectral multi-atlas patch-based label fusion
• Identification of water and fat images in Dixon MRI using aggregated patch-based convolutional neural networks
• Estimating Lung Respiratory Motion Using Combined Global and Local Statistical Models.

This book constitutes the refereed proceedings of the Second International Workshop on Patch-Based Techniques in Medical Images, Patch-MI 2016, which was held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 17 regular papers presented in this volume were carefully reviewed and selected from 25 submissions. The main aim of the Patch-MI 2016 workshop is to promote methodological advances within the medical imaging field, with various applications in image segmentation, image denoising, image super-resolution, computer-aided diagnosis, image registration, abnormality detection, and image synthesis.

• Fundamental methods for image-based biophysical modeling and image synthesis
• Biophysical and data-driven models of disease progression or organ development
• Biophysical and data-driven models of organ motion and deformation
• Biophysical and data-driven models of image formation and acquisition
• Segmentation/registration across or within modalities to aid the learning of model parameters
• Cross modality (PET/MR, PET/CT, CT/MR, etc.) image synthesis
• Simulation and synthesis from large-scale image databases
• Automated techniques for quality assessment of simulations and synthetic images
• Image registration and segmentation
• Image denoising and information fusion
• Image reconstruction from sparse data or sparse views
• Real-time simulation of biophysical properties
• Simulation based approaches for medical imaging
• Synthesis and its applications in computational medical imaging.

This book constitutes the refereed proceedings of the First International Workshop on Simulation and Synthesis in Medical Imaging, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 17 revised full papers presented together in this book were carefully reviewed and selected from 21 submissions. The contributions span the following broad categories: fundamental methods for image-based biophysical modeling and image synthesis; biophysical and data-driven models of disease progression or organ development; biophysical and data-driven models of organ motion and deformation; biophysical and data-driven models of image formation and acquisition; segmentation/registration across or within modalities to aid the learning of model parameters; cross modality (PET/MR, PET/CT, CT/MR, etc.) image synthesis; simulation and synthesis from large-scale image databases; automated techniques for quality assessment of simulations and synthetic images; as well as several applications of image synthesis and simulation in medical imaging such as image registration and segmentation; image denoising and information fusion; image reconstruction from sparse data or sparse views; and real-time simulation of biophysical properties. The papers were divided into two general topics named "simulation based approaches for medical imaging" and "synthesis and its applications in computational medical imaging."

• Part I Clinical Applications of Medical Imaging
• Part II Classification and Clustering- Part III Computer Aided Diagnosis (CAD) Tools and Case Studies
• Part-V Bio-inspiring Based Computer Aided Diagnosis Techniques.

This volume comprises of 21 selected chapters, including two overview chapters devoted to abdominal imaging in clinical applications supported computer aided diagnosis approaches as well as different techniques for solving the pectoral muscle extraction problem in the preprocessing part of the CAD systems for detecting breast cancer in its early stage using digital mammograms. The aim of this book is to stimulate further research in medical imaging applications based algorithmic and computer based approaches and utilize them in real-world clinical applications. The book is divided into four parts, Part-I: Clinical Applications of Medical Imaging, Part-II: Classification and clustering, Part-III: Computer Aided Diagnosis (CAD) Tools and Case Studies and Part-IV: Bio-inspiring based Computer Aided diagnosis techniques.

• Brain lesion image analysis
• Brain tumor image segmentation
• Ischemic stroke lesion image segmentation.

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Brain Lesion (BrainLes), Brain Tumor Segmentation (BRATS) and Ischemic Stroke Lesion Segmentation (ISLES), held in Munich, Germany, on October 5, 2015, in conjunction with the International Conference on Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015. The 25 papers presented in this volume were carefully reviewed and selected from 28 submissions. They are grouped around the following topics: brain lesion image analysis; brain tumor image segmentation; ischemic stroke lesion image segmentation.

• Introduction
• Image pre-processing
• Main Image processing
• Additional Image Processing and Measurement
• Impact of Image Acquisition and Selection of Algorithm Parameters on the Results
• Summary of Measured Features
• Conclusions.

This monograph focuses on the use of analysis and processing methods for images from the Corvis® ST tonometer. The presented analysis is associated with the quantitative, repeatable and fully automatic evaluation of the response of the eye, eyeball and cornea to an air-puff. All the described algorithms were practically implemented in MATLAB®. The monograph also describes and provides the full source code designed to perform the discussed calculations. As a result, this monograph is intended for scientists, graduate students and students of computer science and bioengineering as well as doctors wishing to expand their knowledge of modern diagnostic methods assisted by various image analysis and processing methods.

• A Multi-level Canonical Correlation Analysis Scheme for Standard-dose PET Image Estimation
• Image Super-Resolution by Supervised Adaption of Patchwise Self-Similarity from High-Resolution Image
• Automatic Hippocampus Labeling Using the Hierarchy of Sub-Region Random Forests
• Isointense Infant Brain Segmentation by Stacked Kernel Canonical Correlation Analysis
• Improving Accuracy of Automatic Hippocampus Segmentation in Routine MRI by Features Learned from Ultra-high Field MRI
• Dual-Layer l1-Graph Embedding for Semi-Supervised Image Labeling
• Automatic Liver Tumor Segmentation in Follow-up CT Studies Using Convolutional Neural Network
• Block-based Statistics for Robust Non-Parametric Morphometry
• Automatic Collimation Detection in Digital Radiographs with the Directed Hough Transform and Learning-based Edge Detection
• Efficient Lung Cancer Cell Detection with Deep Convolutional Neural Network
• An Effective Approach for Robust Lung Cancer Cell Detection
• Laplacian Shape Editing with Local Patch Based Force Field for Interactive Segmentation
• Hippocampus Segmentation through Distance Field Fusion
• Learning a Spatiotemporal Dictionary for Magnetic Resonance Fingerprinting with Compress Sensing
• Fast Regions-of-Interest Detection in Whole Slide Histopathology Images
• Reliability Guided Forward and Backward Patch-based Method for Multi-atlas Segmentation
• Correlating Tumour Histology and ex vivo MRI Using Dense Modality-Independent Patch-Based Descriptor
• Multi-Atlas Segmentation using Patch-Based Joint Label Fusion with Non-Negative Least Squares Regression
• A Spatially Constrained Deep Learning Framework for Detection of Epithelial Tumor Nuclei in Cancer Histology Images
• 3D MRI Denoising using Rough Set Theory and Kernel Embedding Method
• A Novel Cell Orientation Congruence Descriptor for Superpixel based Epithelium Segmentation in Endometrial Histology Images
• Patch-based Segmentation from MP2RAGE Images: Comparison to Conventional Techniques
• Multi-Atlas and Multi-Modal Hippocampus Segmentation for Infant MR Brain Images by Propagating Anatomical Labels on Hypergraph
• Prediction of Infant MRI Appearance and Anatomical Structure Evolution using Sparse Patch-based Metamorphosis Learning Framework
• Efficient Multi-Scale Patch-based Segmentation.

This book constitutes the thoroughly refereed post-workshop proceedings of the First International Workshop on Patch-based Techniques in Medical Images, Patch-MI 2015, which was held in conjunction with MICCAI 2015, in Munich, Germany, in October 2015. The 25 full papers presented in this volume were carefully reviewed and selected from 35 submissions. The topics covered are such as image segmentation of anatomical structures or lesions; image enhancement; computer-aided prognostic and diagnostic; multi-modality fusion; mono and multi modal image synthesis; image retrieval; dynamic, functional physiologic and anatomic imaging; super-pixel/voxel in medical image analysis; sparse dictionary learning and sparse coding; analysis of 2D, 2D+t, 3D, 3D+t, 4D, and 4D+t data.

• Probabilistic Graphical Models
• Colocalization Estimation Using Graphical Modeling and Variational Bayesian Expectation Maximization: Towards a Parameter-Free Approach
• Template-Based Multimodal Joint Generative Model of Brain Data
• Generative Method to Discover Genetically Driven Image Biomarkers
• MRI Reconstruction A Joint Acquisition-Estimation Framework for MR Phase Imaging
• A Compressed-Sensing Approach for Super-Resolution Reconstruction of Diffusion MRI
• Accelerated High Spatial Resolution Diffusion-Weighted Imaging
• Clustering
• Joint Spectral Decomposition for the Parcellation of the Human Cerebral Cortex Using Resting-State fMRI
• Joint Clustering and Component Analysis of Correspondenceless Point Sets: Application to Cardiac Statistical Modeling
• Statistical Methods
• Bootstrapped Permutation Test for Multiresponse Inference on Brain Behavior Associations
• Controlling False Discovery Rate in Signal Space for Transformation-Invariant Thresholding of Statistical Maps
• Longitudinal Analysis
• Group Testing for Longitudinal Data
• Spatio-Temporal Signatures to Predict Retinal Disease Recurrence
• Microstructure Imaging
• A Unifying Framework for Spatial and Temporal Diffusion in Diffusion MRI
• Ground Truth for Diffusion MRI in Cancer: A Model-Based Investigation of a Novel Tissue-Mimetic Material
• Shape Analysis
• Anisotropic Distributions on Manifolds: Template Estimation and Most Probable Paths
• A Riemannian Framework for Intrinsic Comparison of Closed Genus-Zero Shapes
• Multi-atlas Fusion Multi-atlas Segmentation as a Graph Labelling Problem: Application to Partially Annotated Atlas Data
• Keypoint Transfer Segmentation
• Fast Image Registration
• Finite-Dimensional Lie Algebras for Fast Diffeomorphic Image Registration
• Fast Optimal Transport Averaging of Neuroimaging Data
• Deformation Models
• Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms
• A Robust Probabilistic Model for Motion Layer Separation in X-ray Fluoroscopy
• Poster Papers
• Weighted Hashing with Multiple Cues for Cell-Level Analysis of Histopathological Images
• Multiresolution Diffeomorphic Mapping for Cortical Surfaces
• A Comprehensive Computer-Aided Polyp Detection System for Colonoscopy Videos
• A Feature-Based Approach to Big Data Analysis of Medical Images
• Joint Segmentation and Registration Through the Duality of Congealing and Maximum Likelihood Estimate
• Self-Aligning Manifolds for Matching Disparate Medical Image Datasets
• Leveraging EAP-Sparsity for Compressed Sensing of MS-HARDI in (k, q)-Space
• Multi-stage Biomarker Models for Progression Estimation in Alzheimer's Disease
• Measuring Asymmetric Interactions in Resting State Brain Networks
• Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis
• Temporal Trajectory and Progression Score Estimation from Voxelwise Longitudinal Imaging Measures: Application to Amyloid Imaging
• Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks
• Bodypart Recognition Using Multi-stage Deep Learning
• Multi-subject Manifold Alignment of Functional Network Structures via Joint Diagonalization
• Brain Transfer: Spectral Analysis of Cortical Surfaces and Functional Maps
• Finding a Path for Segmentation Through Sequential Learning
• Pancreatic Tumor Growth Prediction with Multiplicative Growth and Image-Derived Motion
• IMaGe: Iterative Multilevel Probabilistic Graphical Model for Detection and Segmentation of Multiple Sclerosis Lesions in Brain MRI
• Moving Frames for Heart Fiber Reconstruction
• Detail-Preserving PET Reconstruction with Sparse Image Representation and Anatomical Priors
• Automatic Detection of the Uterus and Fallopian Tube Junctions in Laparoscopic Images
• A Mixed-Effects Model with Time Reparametrization for Longitudinal Univariate Manifold-Valued Data
• Prediction of Longitudinal Development of Infant Cortical Surface Shape Using a 4D Current-Based Learning Framework
• Multi-scale Convolutional Neural Networks for Lung Nodule Classification
• Tractography-Driven Groupwise Multi-scale Parcellation of the Cortex
• Illumination Compensation and Normalization Using Low-Rank Decomposition of Multispectral Images in Dermatology
• Efficient Gaussian Process-Based Modelling and Prediction of Image Time Series
• A Simulation Framework for Quantitative Validation of Artefact Correction in Diffusion MRI
• Towards a Quantified Network Portrait of a Population
• Segmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT Registration
• AxTract: Microstructure-Driven Tractography Based on the Ensemble Average Propagator
• Sampling from Determinantal Point Processes for Scalable Manifold Learning
• Model-Based Estimation of Microscopic Anisotropy in Macroscopically Isotropic Substrates Using Diffusion MRI
• Multiple Orderings of Events in Disease Progression
• Construction of An Unbiased Spatio-Temporal Atlas of the Tongue During Speech
• Tree-Encoded Conditional Random Fields for Image Synthesis
• Simultaneous Longitudinal Registration with Group-Wise Similarity Prior
• Spatially Weighted Principal Component Regression for High-Dimensional Prediction
• Coupled Stable Overlapping Replicator Dynamics for Multimodal Brain Subnetwork Identification
• Joint 6D k-q Space Compressed Sensing for Accelerated High Angular Resolution Diffusion MRI
• Functional Nonlinear Mixed Effects Models for Longitudinal Image Data.

This book constitutes the proceedings of the 24th International Conference on Information Processing in Medical Imaging, IPMI 2015, held at the Sabhal Mor Ostaig College on the Isle of Skye, Scotland, UK, in June/July 2015. The 22 full papers and 41 poster papers presented in this volume were carefully reviewed and selected from 195 submissions. They were organized in topical sections named: probabilistic graphical models; MRI reconstruction; clustering; statistical methods; longitudinal analysis; microstructure imaging; shape analysis; multi-atlas fusion; fast image registration; deformation models; and the poster session.