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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

• Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis.- 3D Brain MRI GAN-based Synthesis Conditioned on Partial Volume Maps.- Synthesizing Realistic Brain MR Images With Noise Control.- Simulated Diffusion Weighted Images Based on Model-Predicted Tumor Growth.- Blind MRI Brain Lesion Inpainting Using Deep Learning.- High-Quality Interpolation of Breast DCE-MRI Using Learned Transformations.- A Method for Tumor Treating Fields Fast Estimation.- Heterogeneous Virtual Population of Simulated CMR Images for Improving the Generalization of Cardiac Segmentation Algorithms.- DyeFreeNet: Deep Virtual Contrast CT Synthesis.- A Gaussian Process Model Based Generative Framework for Data Augmentation of Multi-modal 3D Image Volumes.- Frequency-selective Learning for CT to MR Synthesis.- Uncertainty-aware Multi-resolution Whole-body MR to CT Synthesis.- UltraGAN: Ultrasound Enhancement Through Adversarial Generation.- Improving Endoscopic Decision Support Systems by Translating Between Imaging Modalities.- An Unsupervised Adversarial Learning Approach to Fundus Fluorescein Angiography Image Synthesis for Leakage Detection.- Towards Automatic Embryo Staging in 3D+t Microscopy Images Using Convolutional Neural Networks and PointNets.- Train Small, Generate Big: Synthesis of Colorectal Cancer Histology Images.- Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis.- Auditory Nerve Fiber Health Estimation Using Patient Specific Cochlear Implant Stimulation Models.
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This book constitutes the refereed proceedings of the 5th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The 19 full papers presented were carefully reviewed and selected from 27 submissions. The contributions span the following broad categories in alignment with the initial call-for-papers: methods based on generative models or adversarial learning for MRI/CT/PET/microscopy image synthesis, and several applications of image synthesis and simulation for data augmentation, image enhancement or segmentation.
(source: Nielsen Book Data)

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

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

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

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

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

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

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

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

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

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

• Spectral methods
• Longitudinal methods
• Shape methods.

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

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

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

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

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

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

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

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