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• 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.

• 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)

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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)

• 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)

• 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)

• 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.
(source: Nielsen Book Data)

• 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.

• 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)

• 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.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

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.

• Analysis on manifolds.- Shape analysis.- Disease diagnosis/progression.- Brain networks an connectivity.- Diffusion imaging.- Quantitative imaging.- Imaging genomics.- Image registration.- Segmentation.- General image analysis. <.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• 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.

• 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
• 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.

• 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."

• 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.).