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• Fast Auto-Differentiable Digitally Reconstructed Radiographs for Solving Inverse Problems in Intraoperative Imaging.- Multi-channel Residual Neural Network Based on Squeeze-and Excitation for Osteoporosis Diagnosis.- Machine Learning Based Approach for Motion Detection andEstimation in Routinely Acquired Low Resolution Near Infrared Fluorescence Optical Imaging.- Automatic Landmark Identification on IntraOralScans.- STAU-Net: A Spatial Structure Attention Network for 3D Coronary Artery Segmentation.- Convolutional Redistribution Network for Multi-View Medical Image Diagnosis.- Feature Patch Based Attention Model for Dental Caries Classification.- Conditional Domain Adaptation Based on Initial Distribution Discrepancy for EEG Emotion Recognition.- Automated Cone and Vessel Analysis in Adaptive Optics like RetinalImages for Clinical Diagnostics Support.
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This book constitutes the proceedings of the 11th Workshop on Clinical Image-Based Procedures, CLIP 2022, which was held in conjunction with MICCAI 2022, in Singapore in September 2022. The 9 full papers included in this book were carefully reviewed and selected from 12 submissions. They focus on the applicability of basic research methods in the clinical practice by creating holistic patient models as an important step towards personalized healthcare. .
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• Quantifying the Effect of Image Similarity on Diabetic Foot Ulcer Classification.- DFUC2022 Challenge Papers.- HarDNet-DFUS: Enhancing Backbone and Decoder of HarDNet-MSEGfor Diabetic Foot Ulcer Image Segmentation.- OCRNet For Diabetic Foot Ulcer Segmentation Combined with Edge Loss 30.- On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness.- Capture the Devil in the Details via Partition-then-Ensemble on Higher Resolution Images.- Unconditionally Generated and Pseudo-Labeled Synthetic Images for Diabetic Foot Ulcer Segmentation Dataset Extension.-Post Challenge Paper.- Diabetic Foot Ulcer Segmentation Using Convolutional and Transformer-based Refined Mixup Augmentation for Diabetic Foot Ulcer Segmentation.- Organization IX DFU-Ens: End-to-End Diabetic Foot Ulcer Segmentation Framework with Vision Transformer Based Detection.- Summary Paper.- Diabetic Foot Ulcer Grand Challenge 2022 Summary.
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This book constitutes the Third Diabetic Foot Ulcers Grand Challenge, DFUC 2022, which was held on September 2022, in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 in Singapore. The 8 full papers presented together with 5 challenge papers and 3 post-challenge papers included in this book were carefully reviewed and selected from 19 submissions.The DFU challenges aim to motivate the health care domain to share datasets, participate in ground truth annotation, and enable data-innovation in computer algorithm development. In the longer term, it will lead to improved patient care.
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• Cardiac multiscale structure
• Cardiac electrophysiology modeling
• Image and shape analysis
• Cardiovascular hemodynamics and CFD
• Cardiac biomechanics
• Clinical applications

This book constitutes the refereed proceedings of the 12th International Conference on Functional Imaging and Modeling of the Heart, held in Lyon, France, in June 2023. The 72 full papers were carefully reviewed and selected from 80 submissions. The focus of the papers is on following topics: increased imaging resolutions, data explosion, sophistication of computational models and advent of AI frameworks, while new imaging modalities have emerged (e.g. combined PET-MRI, Spectral CT).

• Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data.- Learning correspondences of cardiac motion using biomechanics-informed modeling.- Multi-modal Latent-space Self-alignment for Super-resolution Cardiac MR Segmentation.- Towards real-time optimization of left atrial appendage occlusion device placement through physics-informed neural networks.- Haemodynamic changes in the fetal circulation after connection to an artificial placenta: a computational modelling study.- Personalized Fast Electrophysiology Simulations to Evaluate Arrhythmogenicity of Ventricular Slow Conduction Channels.- Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations.- Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling.- Comparison of Semi- and Un-supervised Domain Adaptation Methods for Whole-Heart Segmentation.- Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging.- An Atlas-Based Analysis of Biventricular Mechanics in Tetralogy of Fallot.- Review of data types and model dimensionality for cardiac DTI SMS-related artefact removal.- Improving Echocardiography Segmentation by Polar Transformation.- Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach.- Interpretable Prediction of Post-Infarct Ventricular Arrhythmia using Graph Convolutional Network.- Unsupervised Echocardiography Registration through Patch-based MLPs and Transformers.- Sensitivity analysis of left atrial wall modeling approaches and inlet/outlet boundary conditions in fluid simulations to predict thrombus formation.- APHYN-EP: Physics-based deep learning framework to learn and forecast cardiac electrophysiology dynamics.- Unsupervised machine-learning exploration of morphological and haemodynamic indices to predict thrombus formation at the left atrial appendage.- Geometrical deep learning for the estimation of residence time in the left atria.- Explainable Electrocardiogram Analysis with Wave Decomposition: Application to Myocardial Infarction Detection.- A systematic study of race and sex bias in CNN-based cardiac MR segmentation.- Mesh U-Nets for 3D Cardiac Deformation Modeling.- Skeletal model-based analysis of the tricuspid valve in hypoplastic left heart syndrome.- Simplifying Disease Staging Models into a Single Anatomical Axis - A Case Study of Aortic Coarctation In-utero.- Point2Mesh-Net: Combining Point Cloud and Mesh-Based Deep Learning for Cardiac Shape Reconstruction.- Post-Infarction Risk Prediction with Mesh Classification Networks.- Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries.- Computerized Analysis of the Human Heart to Guide Targeted Treatment of Atrial Fibrillation.- 3D Mitral Valve Surface Reconstruction from 3D TEE via Graph Neural Networks.- Efficient MRI Reconstruction with Reinforcement Learning for Automatic Acquisition Stopping.- Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels.- PAT-CNN: Automatic Segmentation and Quantification of Pericardial Adipose Tissue from T2-Weighted Cardiac Magnetic Resonance Images.- Deep Computational Model for the Inference of Ventricular Activation Properties.- Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels.- Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts.- Deep Learning Based Classification and Segmentation for Cardiac Magnetic Resonance Imaging with Respiratory Motion Artifacts.- Multi-task Swin Transformer for Motion Artifacts Classification and Cardiac Magnetic Resonance Image Segmentation.- Automatic Quality Assessment of Cardiac MR Images with Motion Artefacts using Multi-task Learning and K-Space Motion Artefact Augmentation.- Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRI.- Automatic Image Quality Assessment and Cardiac Segmentation Based on CMR Images.- Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation.- 3D MRI cardiac segmentation under respiratory motion artifacts.- Cardiac MR Image Segmentation and Quality Control in the Presence of Respiratory Motion Artifact using Simulated Data.- Combination Special Data Augmentation and Sampling Inspection Network for Cardiac Magnetic Resonance Imaging Quality Classification.- Automatic Cardiac Magnetic Resonance Respiratory Motions Assessment and Segmentation.- Robust Cardiac MRI Segmentation with Data-Centric Models to Improve Performance via Intensive Pre-training and Augmentation.- A deep learning-based fully automatic framework for motion-existing cine image quality control and quantitative analysis.
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This book constitutes the proceedings of the 13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th MICCAI conference. The 34 regular workshop papers included in this volume were carefully reviewed and selected after being revised and deal with topics such as: common cardiac segmentation and modelling problems to more advanced generative modelling for ageing hearts, learning cardiac motion using biomechanical networks, physics-informed neural networks for left atrial appendage occlusion, biventricular mechanics for Tetralogy of Fallot, ventricular arrhythmia prediction by using graph convolutional network, and deeper analysis of racial and sex biases from machine learning-based cardiac segmentation. In addition, 14 papers from the CMRxMotion challenge are included in the proceedings which aim to assess the effects of respiratory motion on cardiac MRI (CMR) imaging quality and examine the robustness of segmentation models in face of respiratory motion artefacts. A total of 48 submissions to the workshop was received.
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• Preface MIDOG 2021.- Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmainGeneralization Challenge.- Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images.- Domain-Robust Mitotic Figure Detection with StyleGAN.- Two-step Domain Adaptation for Mitosis Cell Detection in Histopathology Images.- Robust Mitosis Detection Using a Cascade Mask-RCNN Approach With Domain-Specific Residual Cycle-GAN Data Augmentation.- Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge.- MitoDet: Simple and robust mitosis detection.- Multi-source Domain Adaptation Using Gradient Reversal Layer for Mitotic Cell Detection.- Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge.- Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classi cation Model for MIDOG Challenge.- Domain Adaptive Cascade R-CNN for Mitosis DOmain Generalization (MIDOG) Challenge.- Reducing Domain Shift For Mitosis Detection Using Preprocessing Homogenizers.- Cascade RCNN for MIDOG Challenge.- Sk-Unet Model with Fourier Domain for Mitosis Detection.- Preface MOOD21.- Self-Supervised 3D Out-of-Distribution Detection via Pseudoanomaly Generation.- Self-Supervised Medical Out-of-Distribution Using U-Net Vision Transformers.- SS3D: Unsupervised Out-of-Distribution Detection and Localization for Medical Volumes.- MetaDetector: Detecting Outliers by Learning to Learn from Self-supervision.- AutoSeg - Steering the Inductive Biases for Automatic Pathology Segmentation.- Preface Learn2Reg 2021.- Deformable Registration of Brain MR Images via a Hybrid Loss.- Fraunhofer MEVIS Image Registration Solutions for the Learn2Reg 2021 Challenge.- Unsupervised Volumetric Displacement Fields Using Cost Function Unrolling.- Conditional Deep Laplacian Pyramid Image Registration Network in Learn2Reg Challenge.- The Learn2Reg 2021 MICCAI Grand Challenge (PIMed Team).- Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021.- Progressive and Coarse-to-fine Network for Medical Image Registration across Phases, Modalities and Patients. -Semi-supervised Multilevel Symmetric Image Registration Method for Magnetic Resonance Whole Brain Images.
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This book constitutes three challenges that were held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, which was planned to take place in Strasbourg, France but changed to an online event due to the COVID-19 pandemic. The peer-reviewed 18 long and 9 short papers included in this volume stem from the following three biomedical image analysis challenges: Mitosis Domain Generalization Challenge (MIDOG 2021), Medical Out-of-Distribution Analysis Challenge (MOOD 2021), and Learn2Reg (L2R 2021). The challenges share the need for developing and fairly evaluating algorithms that increase accuracy, reproducibility and efficiency of automated image analysis in clinically relevant applications.
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• Supervoxel Merging towards Brain Tumor Segmentation.- Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI.- Modeling multi-annotator uncertainty as multi-class segmentation problem.- Modeling multi-annotator uncertainty as multi-class segmentation problem.- Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma.- Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks.- Optimization of Deep Learning based Brain Extraction in MRI for Low Resource Environments. Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task.- Unet3D with Multiple Atrous Convolutions Attention Block for Brain Tumor Segmentation.- BRAT
• S2021: exploring each sequence in multi-modal input for baseline U-net performance.- Automatic Brain Tumor Segmentation using Multi-scale Features and Attention Mechanism.- Simple and Fast Convolutional Neural Network applied to median cross sections for predicting the presence of MGMT promoter methylation in FLAIR MRI scans.- MSViT: Multi Scale Vision Transformer forBiomedical Image Segmentation.- Unsupervised Multimodal.- HarDNet-BTS: A Harmonic Shortcut Network for Brain Tumor Segmentation.- Multimodal Brain Tumor Segmentation Algorithm.- Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images.- Multi-plane UNet++ Ensemble for Glioblastoma Segmentation.- Multimodal Brain Tumor Segmentation using Modified UNet Architecture.- A video data based transfer learning approach for classification of MGMT status in brain tumor MR images.- Multimodal Brain Tumor Segmentation Using a 3D ResUNet in BraTS 2021.- 3D MRI brain tumour segmentation with autoencoder regularization and Hausdorff distance loss function.- 3D CMM-Net with Deeper Encoder for Semantic Segmentation of Brain Tumors in BraTS2021 Challenge.- Cascaded training pipeline for 3D brain tumor segmentation.- nnU-Net with Region-based Training and Loss Ensembles for Brain Tumor Segmentation.- Brain Tumor Segmentation Using Attention Activated U-Net with Positive Mining.- Automatic segmentation of brain tumor using 3D convolutional neural networks.- Hierarchical and Global Modality Interaction for Brain Tumor Segmentation.- Ensemble Outperforms Single Models in Brain Tumor Segmentation.- Brain Tumor Segmentation using UNet-Context Encoding Network.- Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRI.
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This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually. This is an open access book.
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• BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation.- Optimized U-Net for Brain Tumor Segmentation.- MS UNet: Multi-Scale 3D UNet for Brain Tumor Segmentation.- Evaluating Scale Attention Network for Automatic Brain Tumor Segmentation with Large Multi-parametric MRI Database.- Orthogonal-Nets: A large ensemble of 2D neural networks for 3D Brain Tumor Segmentation.- Feature Learning by Attention and Ensemble with 3D U-Net to Glioma Tumor Segmentation.- MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks.- Brain Tumor Segmentation with Patch-based 3D Attention UNet from Multi-parametric MRI.- Dice Focal Loss with ResNet-like Encoder-Decoder architecture in 3D Brain Tumor Segmentation.- HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging.- Disparity Autoencoders for Multi-class Brain Tumor Segmentation.- Disparity Autoencoders for Multi-class Brain Tumor Segmentation.- Disparity Autoencoders for Multi-class Brain Tumor Segmentation.- Brain Tumor Segmentation in Multi-parametric Magnetic Resonance Imaging using Model Ensembling and Super-resolution.- Quality-aware Model Ensemble for Brain Tumor Segmentation.- Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs.- An Ensemble Approach to Automatic Brain Tumor Segmentation.- Extending nn-UNet for brain tumor segmentation.- Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge.- Coupling nnU-Nets with Expert Knowledge for Accurate Brain Tumor Segmentation from MRI.- Deep Learning based Ensemble Approach for 3D MRI Brain Tumor Segmentation.- Prediction of MGMT Methylation Status of Glioblastoma using Radiomics and Latent Space Shape Features.- bining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation.- Automatic Brain Tumor Segmentation with a Bridge-Unet deeply supervised enhanced with downsampling pooling combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm.
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This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually.
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• Distributed, Collaborative, and Federated Learning.- Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation
• FedAP: Adaptive Personalization in Federated Learning for Non-IID Data Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes?
• Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes?.- Can collaborative learning be private, robust and scalable?.- Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation.- Joint Multi Organ and Tumor Segmentation from Partial Labels using Federated Learning.- Fuh, Kensaku Mori, Weichung Wang, Holger R Roth GAN Latent Space Manipulation and Aggregation for Federated Learning in Medical Imaging.- A Specificity-Preserving Generative Model for Federated MRI Translation.- Content-Aware Differential Privacy with Conditional Invertible Neural Networks.- DeMed: A Novel and Efficient Decentralized Learning Framework for Medical Images Classification on Blockchain.- Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images.- Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling.- Towards Real-World Federated Learning in Medical Image Analysis Using Kaapana.- Towards Sparsified Federated Neuroimaging Models via Weight Pruning.- Affordable AI and Healthcare.- Enhancing Portable OCT Image Quality via GANs for AI-Based Eye Disease Detection.- Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions.- Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks.- LRH-Net: A Multi-Level Knowledge Distillation Approach for Low-Resource Heart Network.
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This book constitutes the refereed proceedings of the Third MICCAI Workshop on Distributed, Collaborative, and Federated Learning, DeCaF 2022, and the Second MICCAI Workshop on Affordable AI and Healthcare, FAIR 2022, held in conjunction with MICCAI 2022, in Singapore in September 2022. FAIR 2022 was held as a hybrid event.DeCaF 2022 accepted 14 papers from the 18 submissions received. The workshop aims at creating a scientific discussion focusing on the comparison, evaluation, and discussion of methodological advancement and practical ideas about machine learning applied to problems where data cannot be stored in centralized databases or where information privacy is a priority. For FAIR 2022, 4 papers from 9 submissions were accepted for publication. The topics of the accepted submissions focus on deep ultrasound segmentation, portable OCT image quality enhancement, self-attention deep networks and knowledge distillation in low-regime setting.
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This book constitutes the refereed proceedings of the 26th Conference on Medical Image Understanding and Analysis, MIUA 2022, held in Cambridge, UK, in July 2022. The 65 full papers presented were carefully reviewed and selected from 95 submissions. They were organized according to following topical sections: biomarker detection; image registration, and reconstruction; image segmentation; generative models, biomedical simulation and modelling; classification; image enhancement, quality assessment, and data privacy; radiomics, predictive models, and quantitative imaging. Chapter "FCN-Transformer Feature Fusion for Polyp Segmentation" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
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• M^2F: Multi-modal and Multi-task Fusion Network for Glioma Diagnosis and Prognosis.- Visual Modalities based Multimodal Fusion for Surgical Phase Recognition.- Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images.- Vessel Segmentation via Link Prediction of Graph Neural Networks.- A Bagging Strategy-Based Multi-Scale Texture GLCM-CNN Model for Differentiating Malignant from Benign Lesions Using Small Pathologically Proven Dataset.- Liver Segmentation Quality Control in Multi-Sequence MR Studies.- Pattern Analysis of Substantia Nigra in Parkinson Disease by Fifth-Order Tensor Decomposition and Multi-sequence MRI.- Gabor Filter-Embedded U-Net with Transformer-based Encoding for Biomedical Image Segmentation.- Learning-based Detection of MYCN Amplification in Clinical Neuroblastoma Patients: A Pilot Study.- Coordinate Translator for Learning Deformable Medical Image Registration.- Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation.- Improve Multi-modal Patch Based Lymphoma Segmentation with Negative Sample Augmentation and Label Guidance on PET/CT scans.
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This book constitutes the refereed proceedings of the Third International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2022, held in conjunction with MICCAI 2022 in singapore, in September 2022.The 12 papers presented were carefully reviewed and selected from 18 submissions. The MMMI workshop aims to advance the state of the art in multi-scale multi-modal medical imaging, including algorithm development, implementation of methodology, and experimental studies. The papers focus on medical image analysis and machine learning, especially on machine learning methods for data fusion and multi-score learning.
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• Intro
• Preface
• Organization
• Contents
• Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images
• 1 Introduction
• 1.1 Related Works
• 1.2 Contributions
• 2 Methods
• 2.1 Generators
• 2.2 Discriminators
• 2.3 Losses
• 3 Experiments
• 3.1 Evaluation
• 3.2 Implementation
• 3.3 Data
• 3.4 Results
• 4 Conclusion
• References
• Generating Artificial Artifacts for Motion Artifact Detection in Chest CT
• 1 Introduction
• 2 Methods
• 3 Experiments
• 4 Results
• 5 Discussion
• References

• Probabilistic Image Diversification to Improve Segmentation in 3D Microscopy Image Data
• 1 Introduction
• 2 Probabilistic Image Diversification
• 3 Experiments and Results
• 3.1 Data Augmentation
• 3.2 Benchmarking
• 3.3 Test-Time Augmentation
• 4 Discussion and Conclusion
• References
• Pathology Synthesis of 3D Consistent Cardiac MR Images Using 2D VAEs and GANs
• 1 Introduction
• 1.1 Contributions
• 2 Method
• 2.1 Pathology Synthesis
• 2.2 Modeling Slice Relationship
• 2.3 Data and Implementation
• 3 Results
• 3.1 Pathology Synthesis
• 3.2 Modeling the Slice Relationship

• 4 Discussion and Conclusion
• References
• .26em plus .1em minus .1emHealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease
• 1 Introduction
• 2 HealthyGAN: The Proposed Method
• 2.1 Network Architecture
• 2.2 Training
• 2.3 Detecting Anomalies
• 3 Experiments and Results
• 3.1 COVID-19 Detection
• 3.2 Chest X-ray 14 Diseases Detection
• 3.3 Migraine Detection
• 4 Conclusion
• A Implementation Details
• B Network Architectures
• B.1 Discriminator
• B.2 Generator
• References

• Bi-directional Synthesis of Pre- and Post-contrast MRI via Guided Feature Disentanglement
• 1 Introduction
• 2 Methodology
• 3 Experiments and Results
• 4 Conclusion
• References
• Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain
• 1 Introduction
• 2 Background
• 2.1 VQ-VAE
• 2.2 Transformer
• 3 Methods
• 3.1 Descriptive Quantization for Transformer Usage
• 3.2 Autoregressive Modelling of the Brain
• 4 Experiments and Results
• 4.1 Quantitative Image Fidelity Evaluation
• 4.2 Morphological Evaluation
• 5 Conclusion
• 6 Appendix
• 6.1 VQ-VAEs

• 6.2 Transformers
• 6.3 Losses
• 6.4 Datasets
• 6.5 VBM Analysis
• References
• Can Segmentation Models Be Trained with Fully Synthetically Generated Data?
• 1 Background
• 2 Materials and Methods
• 2.1 Materials
• 2.2 Methods
• 2.3 Segmentation Network Used for the Experiments
• 3 Experiments
• 3.1 Can We Learn to Segment Healthy Regions Using Synthetic Data?
• 3.2 Can Synthetic Generative Models Address Out-of-Distribution Segmentation?
• 3.3 Can We Learn to Segment Pathologies from Synthetic Data?
• 4 Discussion and Conclusion
• A Training Set-Ups

This book constitutes the refereed proceedings of the 7th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2022, held in conjunction with MICCAI 2022, in Singapore, Singapore in September 2022.
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• Prostate Cancer MR Imaging.- Computer Aided Detection of Prostate Cancer Using T2, DWI and DCE MRI: Methods and Clinical Applications.- Prostate Cancer Segmentation Using Multispectral Random Walks.- Automatic MRI Atlas-Based External Beam Radiation Therapy Treatment Planning for Prostate Cancer.- An Efficient Inverse-Consistent Diffeomorphic Image Registration Method for Prostate Adaptive Radiotherapy.- Atlas Based Segmentation and Mapping of Organs at Risk from Planning CT for the Development of Voxel-Wise Predictive Models of Toxicity in Prostate Radiotherapy.- Realtime TRUS/MRI Fusion Targeted-Biopsy for Prostate Cancer: A Clinical Demonstration of Increased Positive Biopsy Rates.- HistoCAD: Machine Facilitated Quantitative Histoimaging with Computer Assisted Diagnosis.- Registration of In Vivo Prostate Magnetic Resonance Images to Digital Histopathology Images.- High-Throughput Prostate Cancer Gland Detection, Segmentation, and Classification from Digitized Needle Core Biopsies.- Automated Analysis of PIN-4 Stained Prostate Needle Biopsies.- Augmented Reality Image Guidance in Minimally Invasive Prostatectomy.- Texture Guided Active Appearance Model Propagation for Prostate Segmentation.- Novel Stochastic Framework for Accurate Segmentation of Prostate in Dynamic Contrast Enhanced MRI.- Boundary Delineation in Prostate Imaging Using Active Contour Segmentation Method with Interactively Defined Object Regions.
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Prostatic adenocarcinoma (CAP) is the second most common malignancy with an estimated 190,000 new cases in the USA in 2010 (Source: American Cancer Society), and is the most frequently diagnosed cancer among men. If CAP is caught early, men have a high, five-year survival rate. Unfortunately there is no standardized ima- based screening protocol for early detection of CAP (unlike for breast cancers). In the USA high levels of prostate-specific antigen (PSA) warrant a trans-rectal ultrasound (TRUS) biopsy to enable histologic confirmation of presence or absence of CAP. With recent rapid developments in multi-parametric radiological imaging te- niques (spectroscopy, dynamic contrast enhanced MR imaging, PET, RF ultrasound), some of these functional and metabolic imaging modalities are allowing for definition of high resolution, multi-modal signatures for prostate cancer in vivo. Distinct com- tational and technological challenges for multi-modal data registration and classifi- tion still remain in leveraging this multi-parametric data for directing therapy and optimizing biopsy. Additionally, with the recent advent of whole slide digital sc- ners, digitized histopathology has become amenable to computerized image analysis. While it is known that outcome of prostate cancer (prognosis) is highly correlated with Gleason grade, pathologists often have difficulty in distinguishing between interme- ate Gleason grades from histopathology. Development of computerized image analysis methods for automated Gleason grading and predicting outcome on histopathology have to confront the significant computational challenges associated with working these very large digitized images.
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• Segmentation.- A Shape-Guided Deformable Model with Evolutionary Algorithm Initialization for 3D Soft Tissue Segmentation.- Shape Regression Machine.- Active Mean Fields: Solving the Mean Field Approximation in the Level Set Framework.- Liver Segmentation Using Sparse 3D Prior Models with Optimal Data Support.- Cardiovascular Imaging.- Adaptive Non-rigid Registration of Real Time 3D Ultrasound to Cardiovascular MR Images.- Multi-slice Three-Dimensional Myocardial Strain Tensor Quantification Using zHARP.- Bayesian Tracking of Elongated Structures in 3D Images.- Effective Statistical Edge Integration Using a Flux Maximizing Scheme for Volumetric Vascular Segmentation in MRA.- Detection and Labeling.- Joint Sulci Detection Using Graphical Models and Boosted Priors.- Rao-Blackwellized Marginal Particle Filtering for Multiple Object Tracking in Molecular Bioimaging.- Spine Detection and Labeling Using a Parts-Based Graphical Model.- Lung Nodule Detection Via Bayesian Voxel Labeling.- Poster Session I.- Functional Interactivity in fMRI Using Multiple Seeds' Correlation Analyses - Novel Methods and Comparisons.- Learning Best Features and Deformation Statistics for Hierarchical Registration of MR Brain Images.- Information-Theoretic Analysis of Brain White Matter Fiber Orientation Distribution Functions.- Segmentation of Sub-cortical Structures by the Graph-Shifts Algorithm.- High-Quality Consistent Meshing of Multi-label Datasets.- Digital Homeomorphisms in Deformable Registration.- Incorporating DTI Data as a Constraint in Deformation Tensor Morphometry Between T1 MR Images.- LV Segmentation Through the Analysis of Radio Frequency Ultrasonic Images.- Chestwall Segmentation in 3D Breast Ultrasound Using a Deformable Volume Model.- Automatic Cortical Segmentation in the Developing Brain.- Comparing Pairwise and Simultaneous Joint Registrations of Decorrelating Interval Exams Using Entropic Graphs.- Combining Radiometric and Spatial Structural Information in a New Metric for Minimal Surface Segmentation.- A Fuzzy, Nonparametric Segmentation Framework for DTI and MRI Analysis.- Symmetric Positive 4 th Order Tensors & Their Estimation from Diffusion Weighted MRI.- Atlas-to-Image Non-rigid Registration by Minimization of Conditional Local Entropy.- Shape Modeling and Analysis with Entropy-Based Particle Systems.- A Volumetric Approach to Quantifying Region-to-Region White Matter Connectivity in Diffusion Tensor MRI.- Brain Image Registration Using Cortically Constrained Harmonic Mappings.- Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts.- Multi-fiber Reconstruction from Diffusion MRI Using Mixture of Wisharts and Sparse Deconvolution.- A Hamiltonian Particle Method for Diffeomorphic Image Registration.- Inter and Intra-modal Deformable Registration: Continuous Deformations Meet Efficient Optimal Linear Programming.- Tracer Kinetics Guided Dynamic PET Reconstruction.- Maximum Likelihood Estimators in Magnetic Resonance Imaging.- Quantifying Metabolic Asymmetry Modulo Structure in Alzheimer's Disease.- Adaptive Time-Frequency Models for Single-Trial M/EEG Analysis.- Imaging Brain Activation Streams from Optical Flow Computation on 2-Riemannian Manifolds.- High Level Group Analysis of FMRI Data Based on Dirichlet Process Mixture Models.- Poster Session II.- Insight into Efficient Image Registration Techniques and the Demons Algorithm.- Divergence-Based Framework for Diffusion Tensor Clustering, Interpolation, and Regularization.- Localized Components Analysis.- Regional Appearance in Deformable Model Segmentation.- Fully Automated Registration of First-Pass Myocardial Perfusion MRI Using Independent Component Analysis.- Octree Grid Topology Preserving Geometric Deformable Model for Three-Dimensional Medical Image Segmentation.- High-Dimensional Entropy Estimation for Finite Accuracy Data: R-NN Entropy Estimator.- Kernel-Based Manifold Learning for Statistical Analysis of Diffusion Tensor Images.- An Anatomical Equivalence Class Based Joint Transformation-Residual Descriptor for Morphological Analysis.- Incorporation of Regional Information in Optimal 3-D Graph Search with Application for Intraretinal Layer Segmentation of Optical Coherence Tomography Images.- Localized Maximum Entropy Shape Modelling.- Computer Aided Detection of Pulmonary Embolism with Tobogganing and Mutiple Instance Classification in CT Pulmonary Angiography.- Measures for Pathway Analysis in Brain White Matter Using Diffusion Tensor Images.- Estimating the Mesorectal Fascia in MRI.- A Marginalized MAP Approach and EM Optimization for Pair-Wise Registration.- Geometry Driven Volumetric Registration.- A Recursive Anisotropic Fast Marching Approach to Reaction Diffusion Equation: Application to Tumor Growth Modeling.- Population-Based Fitting of Medial Shape Models with Correspondence Optimization.- Robust Parametric Modeling Approach Based on Domain Knowledge for Computer Aided Detection of Vertebrae Column Metastases in MRI.- Nonrigid Image Registration Using Conditional Mutual Information.- Non-parametric Surface-Based Regularisation for Building Statistical Shape Models.- Geometrically Proper Models in Statistical Training.- Registration-Derived Estimates of Local Lung Expansion as Surrogates for Regional Ventilation.
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This book constitutes the refereed proceedings of the 20th International Conference on Information Processing in Medical Imaging, IPMI 2007, held in Kerkrade, The Netherlands, in July 2007. It covers segmentation, cardiovascular imaging, detection and labeling, diffusion tensor imaging, registration, image reconstruction, functional brain imaging, as well as shape models and registration.
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This book constitutes the thoroughly refereed post proceedings of the international workshop Computer Vision Approaches to Medical Image Analysis, CVAMIA 2006, held in Graz, Austria in May 2006 as a satellite event of the 9th European Conference on Computer Vision, EECV 2006. The 10 revised full papers and 11 revised poster papers presented together with 1 invited talk were carefully reviewed and selected from 38 submissions. The papers are organized in topical sections on clinical applications, image registration, image segmentation and analysis, and the poster session.
(source: Nielsen Book Data)

This book constitutes the refeered proceedings of the 19th International Conference on Information Processing in Medical Imaging, IPMI 2005, held in Glenwood Springs, Colorado, in July 2005. The 63 revised full papers presented were carefully reviewed and selected from 245 submissions. The papers are organized in topical sections on shape and population modeling, diffusion tensor imaging and functional magnetic resonance, segmentation and filtering, small animal imaging, surfaces and segmentation, applications, image registration, registration and segmentation.
(source: Nielsen Book Data)

This book constitutes the thoroughly refereed joint post proceedings of the international workshop Computer Vision Approaches to Medical Image Analysis, CVAMIA 2004, and Mathematical Methods in Biomedical Image Analysis, MMBIA 2004, both held in Prague, Czech Republic, in May 2004 as part of EECV 2004. The 37 revised full papers presented were carefully selected and improved during two rounds of reviewing and revision. The papers are organized in topical sections on image acquision techniques, image reconstruction, mathematical methods, medical image segmentation, image registration, and applications.
(source: Nielsen Book Data)

This book constitutes the refeered proceedings of the 18th Interational Conference on Information Processing in Medical Imaging, IPMI 2003, held in UK, in July 2003.The 57 revised full papers presented were carefully reviewed and selected from submissions. The papers are organized in topical sections shape modeling, shape analysis, segmentation, color, performance characterization, registration and modeling similarity, registration and modeling deformation, cardiac motion, fMRI analysis, and diffusion imaging and tractography.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 17th International Conference on Information Processing in Medical Imaging, IPMI 2001, held in Davis, CA, USA, in June 2001. The 54 revised papers presented were carefully reviewed and selected from 78 submissions. The papers are organized in topical sections on objective assessment of image quality, shape modeling, molecular and diffusion tensor imaging, registration and structural analysis, functional image analysis, fMRI/EEG/MEG, deformable registration, shape analysis, and analysis of brain structure.
(source: Nielsen Book Data)