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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.
(source: Nielsen Book Data)

• 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.
• (source: Nielsen Book Data)

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.
(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)

• 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.

• 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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• 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)

• Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017:
• Simultaneous Multiple Surface Segmentation Using Deep Learning / Abhay Shah, Michael D. Abramoff, Xiaodong Wu
• A Deep Residual Inception Network for HEp-2 Cell Classification / Yuexiang Li, Linlin Shen
• Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures / Roger Trullo, Caroline Petitjean, Dong Nie, Dinggang Shen, Su Ruan
• Accelerated Magnetic Resonance Imaging by Adversarial Neural Network / Ohad Shitrit, Tammy Riklin Raviv
• Left Atrium Segmentation in CT Volumes with Fully Convolutional Networks / Honghui Liu, Jianjiang Feng, Zishun Feng, Jiwen Lu, Jie Zhou
• 3D Randomized Connection Network with Graph-Based Inference / Siqi Bao, Pei Wang, Albert C. S. Chung
• Adversarial Training and Dilated Convolutions for Brain MRI Segmentation / Pim Moeskops, Mitko Veta, Maxime W. Lafarge, Koen A. J. Eppenhof, Josien P. W. Pluim
• CNNs Enable Accurate and Fast Segmentation of Drusen in Optical Coherence Tomography / Shekoufeh Gorgi Zadeh, Maximilian W. M. Wintergerst, Vitalis Wiens, Sarah Thiele, Frank G. Holz, Robert P. Finger et al.
• Region-Aware Deep Localization Framework for Cervical Vertebrae in X-Ray Images / S. M. Masudur Rahman Al Arif, Karen Knapp, Greg Slabaugh
• Domain-Adversarial Neural Networks to Address the Appearance Variability of Histopathology Images / Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof, Pim Moeskops, Mitko Veta
• Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks / Sangheum Hwang, Sunggyun Park
• Deep Residual Recurrent Neural Networks for Characterisation of Cardiac Cycle Phase from Echocardiograms / Fatemeh Taheri Dezaki, Neeraj Dhungel, Amir H. Abdi, Christina Luong, Teresa Tsang, John Jue et al.
• Computationally Efficient Cardiac Views Projection Using 3D Convolutional Neural Networks / Matthieu Le, Jesse Lieman-Sifry, Felix Lau, Sean Sall, Albert Hsiao, Daniel Golden
• Non-rigid Craniofacial 2D-3D Registration Using CNN-Based Regression / Yuru Pei, Yungeng Zhang, Haifang Qin, Gengyu Ma, Yuke Guo, Tianmin Xu et al.
• A Deep Level Set Method for Image Segmentation / Min Tang, Sepehr Valipour, Zichen Zhang, Dana Cobzas, Martin Jagersand
• Context-Based Normalization of Histological Stains Using Deep Convolutional Features / D. Bug, S. Schneider, A. Grote, E. Oswald, F. Feuerhake, J. Schüler et al.
• Transitioning Between Convolutional and Fully Connected Layers in Neural Networks / Shazia Akbar, Mohammad Peikari, Sherine Salama, Sharon Nofech-Mozes, Anne Martel
• Quantifying the Impact of Type 2 Diabetes on Brain Perfusion Using Deep Neural Networks / Behrouz Saghafi, Prabhat Garg, Benjamin C. Wagner, S. Carrie Smith, Jianzhao Xu, Ananth J. Madhuranthakam et al.
• Multi-stage Diagnosis of Alzheimer's Disease with Incomplete Multimodal Data via Multi-task Deep Learning / Kim-Han Thung, Pew-Thian Yap, Dinggang Shen
• A Multi-scale CNN and Curriculum Learning Strategy for Mammogram Classification / William Lotter, Greg Sorensen, David Cox
• Analyzing Microscopic Images of Peripheral Blood Smear Using Deep Learning / Dheeraj Mundhra, Bharath Cheluvaraju, Jaiprasad Rampure, Tathagato Rai Dastidar
• AGNet: Attention-Guided Network for Surgical Tool Presence Detection / Xiaowei Hu, Lequan Yu, Hao Chen, Jing Qin, Pheng-Ann Heng
• Pathological Pulmonary Lobe Segmentation from CT Images Using Progressive Holistically Nested Neural Networks and Random Walker / Kevin George, Adam P. Harrison, Dakai Jin, Ziyue Xu, Daniel J. Mollura
• End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network / Bob D. de Vos, Floris F. Berendsen, Max A. Viergever, Marius Staring, Ivana Išgum
• Stain Colour Normalisation to Improve Mitosis Detection on Breast Histology Images / Azam Hamidinekoo, Reyer Zwiggelaar
• 3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation / Masahiro Oda, Natsuki Shimizu, Holger R. Roth, Ken'ichi Karasawa, Takayuki Kitasaka, Kazunari Misawa et al.
• A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology / Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Minsoo Kim
• Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations / Carole H. Sudre, Wenqi Li, Tom Vercauteren, Sebastien Ourselin, M. Jorge Cardoso
• ssEMnet: Serial-Section Electron Microscopy Image Registration Using a Spatial Transformer Network with Learned Features / Inwan Yoo, David G. C. Hildebrand, Willie F. Tobin, Wei-Chung Allen Lee, Won-Ki Jeong
• Fully Convolutional Regression Network for Accurate Detection of Measurement Points / Michal Sofka, Fausto Milletari, Jimmy Jia, Alex Rothberg
• Fast Predictive Simple Geodesic Regression / Zhipeng Ding, Greg Fleishman, Xiao Yang, Paul Thompson, Roland Kwitt, Marc Niethammer et al.
• Learning Spatio-Temporal Aggregation for Fetal Heart Analysis in Ultrasound Video / Arijit Patra, Weilin Huang, J. Alison Noble
• Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks / Aleksander Klibisz, Derek Rose, Matthew Eicholtz, Jay Blundon, Stanislav Zakharenko
• Self-supervised Learning for Spinal MRIs / Amir Jamaludin, Timor Kadir, Andrew Zisserman
• Skin Lesion Segmentation via Deep RefineNet / Xinzi He, Zhen Yu, Tianfu Wang, Baiying Lei
• Multi-scale Networks for Segmentation of Brain Magnetic Resonance Images / Jie Wei, Yong Xia
• Deep Learning for Automatic Detection of Abnormal Findings in Breast Mammography / Ayelet Akselrod-Ballin, Leonid. Karlinsky, Alon Hazan, Ran Bakalo, Ami Ben Horesh, Yoel Shoshan et al.
• Grey Matter Segmentation in Spinal Cord MRIs via 3D Convolutional Encoder Networks with Shortcut Connections / Adam Porisky, Tom Brosch, Emil Ljungberg, Lisa Y. W. Tang, Youngjin Yoo, Benjamin De Leener et al.
• 7th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017:
• Mapping Multi-Modal Routine Imaging Data to a Single Reference via Multiple Templates / Johannes Hofmanninger, Bjoern Menze, Marc-André Weber, Georg Langs
• Automated Detection of Epileptogenic Cortical Malformations Using Multimodal MRI / Ravnoor S. Gill, Seok-Jun Hong, Fatemeh Fadaie, Benoit Caldairou, Boris Bernhardt, Neda Bernasconi et al.
• Prediction of Amyloidosis from Neuropsychological and MRI Data for Cost Effective Inclusion of Pre-symptomatic Subjects in Clinical Trials / Manon Ansart, Stéphane Epelbaum, Geoffroy Gagliardi, Olivier Colliot, Didier Dormont, Bruno Dubois et al.
• Automated Multimodal Breast CAD Based on Registration of MRI and Two View Mammography / T. Hopp, P. Cotic Smole, N. V. Ruiter
• EMR-Radiological Phenotypes in Diseases of the Optic Nerve and Their Association with Visual Function / Shikha Chaganti, Jamie R. Robinson, Camilo Bermudez, Thomas Lasko, Louise A. Mawn, Bennett A. Landman
• Erratum to: Fast Predictive Simple Geodesic Regression / Zhipeng Ding, Greg Fleishman, Xiao Yang, Paul Thompson, Roland Kwitt, Marc Niethammer et al.

This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 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 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.
(source: Nielsen Book Data)

• Chapter 1. Data Analytics: COVID-19 Prediction using Multimodal Data.-
• Chapter 2. COVID-19 Apps: Privacy and security concerns.-
• Chapter 3. Coronavirus Outbreak: Multi-objective Prediction and Optimization.-
• Chapter 4. AI-Enabled Framework to Prevent COVID-19 from Further Spreading.-
• Chapter 5. Artificial Intelligence Enabled Robotic Drones for COVID-19 Outbreak.-
• Chapter 6. Understanding and Analysis of Enhanced COVID-19 Chest X-Ray Images.-
• Chapter 7. Deep Learning-based COVID-19 Diagnosis and Trend Predictions.-
• Chapter 8. COVID-19: Loose Ends.-
• Chapter 9. Social Distancing and Artificial Intelligence- Understanding the Duality in the times of Covid-19.-
• Chapter 10. Post Covid-19 and Business Analytics.
• (source: Nielsen Book Data)

This book discusses intelligent systems and methods to prevent further spread of COVID-19, including artificial intelligence, machine learning, computer vision, signal processing, pattern recognition, and robotics. It not only explores detection/screening of COVID-19 positive cases using one type of data, such as radiological imaging data, but also examines how data analytics-based tools can help predict/project future pandemics. In addition, it highlights various challenges and opportunities, like social distancing, and addresses issues such as data collection, privacy, and security, which affect the robustness of AI-driven tools. Also investigating data-analytics-based tools for projections using time series data, pattern analysis tools for unusual pattern discovery (anomaly detection) in image data, as well as AI-enabled robotics and its possible uses, the book will appeal to a broad readership, including academics, researchers and industry professionals.
(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)

• Smart Hospital.- Online Health Community.- Mobile Health.- Medical Big Data and Healthcare Machine Learning.- Chronic Disease Management.- Health Informatics.
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-conference proceedings of the International Conference for Smart Health, ICSH 2018, held in Wuhan, China, in July 2018.The 14 full papers and 21 short papers presented were carefully reviewed and selected from 49 submissions. They focus on studies on the principles, approaches, models, frameworks, new applications, and effects of using novel information technology to address healthcare problems and improve social welfare. The selected papers are organized into the following topics: smart hospital; online health community; mobile health; medical big data and healthcare machine learning; chronic disease management; and health informatics.
(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.

• State-of-the-art techniques.- Novel and emerging analysis and visualization techniques.- Clinical challenges and open problems.- Major aspects of problems related to spine imaging.- Including clinical applications of spine imaging.- Computer aided diagnosis of spine conditions.- Computer aided detection of spine-related diseases.- Emerging computational imaging techniques for spinal diseases, .-Fast 3D reconstruction of spine, feature extraction, multiscale analysis, pattern recognition, image enhancement of spine imaging.- Image-guided spine intervention and treatment, multimodal image registration and fusion for spine imaging.- Novel visualization techniques, segmentation techniques for spine imaging, statistical and geometric modeling for spine and vertebra, spine and vertebra localization.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 4th International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 13 workshop papers were carefully reviewed and selected for inclusion in this volume. They aim at reviewing the state-of-the-art techniques, sharing the novel and emerging analysis and visualization techniques and discussing the clinical challenges and open problems in this rapidly growing field - including all major aspects of problems related to spine imaging, including clinical applications of spine imaging, computer aided diagnosis of spine conditions, computer aided detection of spine-related diseases, emerging computational imaging techniques for spinal diseases, fast 3D reconstruction of spine, feature extraction, multiscale analysis, pattern recognition, image enhancement of spine imaging, image-guided spine intervention and treatment, multimodal image registration and fusion for spine imaging, novel visualization techniques, segmentation techniques for spine imaging, statistical and geometric modeling for spine and vertebra, spine and vertebra localization.
(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)