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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.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• 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.

• Introduction.- Describing Spatial Position and Orientation.- Robot Kinematics.- Joint Velocities and Jacobi-Matrices.- Navigation and Registration.- Treatment Planning.- Motion Correlation and Tracking.- Motion Prediction.- Motion Replication.- Applications of Surgical Robotics.- Rehabilitation, Neuroprosthetics and Brain-Machine Interfaces.
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This book provides a thorough background to the emerging field of medical robotics. It covers the mathematics needed to understand the use of robotic devices in medicine, including but not limited to robot kinematics, hand-eye and robot-world calibration, reconstruction, registration, motion planning, motion prediction, motion correlation, motion replication and motion learning. Additionally, basic methods behind state-of-the art robots like the DaVinci system, the CyberKnife, motorized C-arms and operating microscopes as well as stereotactic frames are presented. The book is a text book for undergraduates in computer science and engineering. The main idea of the book is to motivate the methods in robotics in medical applications rather than industrial applications. The book then follows the standard path for a robotics textbook. It is thus suitable for a first course in robotics for undergraduates. It is the first textbook on medical robotics.
(source: Nielsen Book Data)

• 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.
(source: Nielsen Book Data)

• 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.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• 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.

• 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.

• 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)

This book contains revised and extended selected papers from two workshops: the 10th International Workshop on Agents Applied in Health Care, A2HC 2017, held at the 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017, held in Sao Paulo, Brazil, in May 2017, and the International Workshop on Agents and Multi-Agent Systems for AAL and e-Health, A-HEALTH 2017, held at the 15th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2017, in Porto, Portugal, in June 2017. The 9 revised full papers were carefully reviewed and selected from 16 submissions. They feature current research topics such as personalised health systems for remote and autonomous tele-assistance, communication and co-operation between distributed intelligent agents to manage patient care, information agents that retrieve medical information from distributed repositories, intelligent and distributed data mining, and multi-agent systems that assist the doctors in the tasks of monitoring, decision support and diagnosis. .
(source: Nielsen Book Data)

• 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.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• 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.
• (source: Nielsen Book Data)

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)

• 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.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• Computer Aided Identification of Motion Disturbances Related to Parkinson's Disease
• Prediction of Severity and Treatment Outcome for ASD from fMRI
• Enhancement of Perivascular Spaces Using a Very Deep 3D Dense Network
• Generation of Amyloid PET Images via Conditional Adversarial Training for Predicting Progression to Alzheimer's Disease
• Prediction of Hearing Loss Based on Auditory Perception: A Preliminary Study
• Predictive Patient Care: Survival Model to Prevent Medication Non-adherence
• Joint Robust Imputation and Classification for Early Dementia Detection Using Incomplete Multi-Modality Data
• Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer's Disease
• Predicting Nucleus Basalis of Meynert Volume from Compartmental Brain Segmentations
• Multi-modal Neuroimaging Data Fusion via Latent Space Learning for Alzheimer's Disease Diagnosis
• Transfer Learning for Task Adaptation of Brain Lesion Assessment and Prediction of Brain Abnormalities Progression/Regression Using Irregularity Age Map in Brain MRI
• Multi-View Brain Network Prediction From a Source View Using Sample Selection via CCA-based Multi-Kernel Connectomic Manifold Learning
• Predicting Emotional Intelligence Scores From Multi-Session Functional Brain Connectomes
• Predictive Modeling of Longitudinal Data for Alzheimer's Disease Diagnosis Using RNNs
• Towards Continuous Health Diagnosis from Faces with Deep Learning
• XmoNet: A Fully Convolutional Network for Cross-Modality MR Image Inference
• 3D Convolutional Neural Network and Stacked Bidirectional Recurrent Neural Network for Alzheimer's Disease Diagnosis
• Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI
• Diffusion MRI Spatial Super-Resolution Using Generative Adversarialv Networks
• Prediction to Atrial Fibrillation Using Deep Convolutional Neural Networks.

This book constitutes the refereed proceedings of the First International Workshop on PRedictive Intelligence in MEdicine, PRIME 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 20 full papers presented were carefully reviewed and selected from 23 submissions. The main aim of the workshop is to propel the advent of predictive models in a broad sense, with application to medical data. Particularly, the workshop will admit papers describing new cutting-edge predictive models and methods that solve challenging problems in the medical field.

This book constitutes the refereed joint proceedings of the 6th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2017, and the Second International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 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 6 full papers presented at CVII-STENT 2017 and the 11 full papers presented at LABELS 2017 were carefully reviewed and selected. The CVII-STENT papers feature the state of the art in imaging, treatment, and computer-assisted intervention in the field of endovascular interventions. The LABELS papers present a variety of approaches for dealing with few labels, from transfer learning to crowdsourcing.
(source: Nielsen Book Data)

• Part I: Biomedical Engineering, Trends, Research and Technologies
• Vision Paper for Enabling Internet of Medical Robotics Things in Open Healthcare Platform 2030
• Stumbling Blocks of Utilizing Medical and Health Data: Success Factors Extracted from Australia-Japan Comparison
• Digital Financial Incentives for Improved Population Health in the Americas
• Part II: Advanced ICT for Medicine and Healthcare
• Trial Run of a Patient Call System using Mobile Devices
• Advance Watermarking Algorithm using SURF with DWT and DCT for CT Images
• Improving Depth Perception using Multiple Iso-Surfaces for Transparent Stereoscopic Visualization of Medical Volume Data
• Design and Simulation of a Robotic Manipulator for Laparoscopic Uterine Surgeries
• Self-Skill Training System for Chest Compressions in Neonatal Resuscitation Workshop
• Part III. Statistical Signal Processing and Artificial Intelligence
• Comparative Study of Pattern Recognition Methods for Predicting Glaucoma Diagnosis
• Research on Encrypted Face Recognition Algorithm Based on New Combined Chaotic Map and Neural Network
• A 3D Shrinking-and-Expanding Module with Channel Attention for Efficient Deep Learning-Based Super-Resolution
• Dynamic Facial Features in Positive-Emotional Speech for Identification of Depressive Tendencies
• Hand-Crafted and Deep Learning-Based Radiomics Models for Recurrence Prediction of Non-Small Cells Lung Cancers
• Weakly and Semi-supervised Deep Level Set Network for Automated Skin Lesion Segmentation
• Part IV. Support System for Medicine and Healthcare
• A Transcriptional Study of Oncogenes and Tumor Suppressors Altered by Copy Number Variations in Ovarian Cancer
• Analysis of Acoustic Features Affected by Residual Food in the Piriform Fossa Toward Early-Detection of Dysphagia
• Automatic Joint Space Distance Measurement Method for Rheumatoid Arthritis Medical Examinations
• Development of an Active Compression System for Venous Disease
• Design and Development of a Droplet-Based Microfluidics System Using Laser Fabrication Machining Techniques for a Lab on a Chip Device
• Design of a Novel MEMS-Based Microgripper with Hybrid Actuation to Determine Circulating Tumor Cell (CTC) Progression.

This book presents the proceedings of the KES International Conferences on Innovation in Medicine and Healthcare (KES-InMed-19), held in Split, Croatia, on June 17-19, 2020. Covering a number of key areas, including digital IT architecture in healthcare; advanced ICT for medicine and healthcare; biomedical engineering, trends, research and technologies; and healthcare support systems, this book is a valuable resource for researchers, managers, industrialists and anyone wishing to gain an overview of the latest research in these fields. .
(source: Nielsen Book Data)

• 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.
(source: Nielsen Book Data)

• 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.
(source: Nielsen Book Data)

• 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.
(source: Nielsen Book Data)

• Active learning
• Semi-supervised learning
• Reinforcement learning
• Domain adaptation and transfer learning
• Crowd-sourcing annotations and fusion of labels from different sources
• Data augmentation
• Modelling of label uncertainty
• Visualization and human-computer interaction
• Image description
• Medical imaging-based diagnosis
• Medical signal-based diagnosis
• Medical image reconstruction and model selection using deep learning techniques
• Meta-heuristic techniques for fine-tuning
• Parameter in deep learning-based architectures
• Applications based on deep learning techniques.

This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty. The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.

• 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.