Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV

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

Segmentation.- Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression.- DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervision.- KISEG: A Three-Stage Segmentation Framework for Multi-level Acceleration  of Chest CT Scans from COVID-19 Patients.- CircleNet: Anchor-free Glomerulus Detection with Circle Representation.- Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies.- Diagnostic Assessment of Deep Learning Algorithms for Detection and Segmentation of Lesion in Mammographic images.- Efficient and Phase-aware Video Super-resolution for Cardiac MRI.- ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease.- Deep Generative Model-based Quality Control for Cardiac MRI Segmentation.- DeU-Net: Deformable U-Net for 3D Cardiac MRI Video Segmentation.- Learning Directional Feature Maps for Cardiac MRI Segmentation.- Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention.- XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms.- TexNet: Texture Loss Based Network for Gastric Antrum Segmentation in Ultrasound.- Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasets.- Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling.- Pay More Attention to Discontinuity for Medical Image Segmentation.- Learning 3D Features with 2D CNNs via Surface Projection for CT Volume Segmentation.- Deep Class-specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation.- Memory-efficient Automatic Kidney and Tumor Segmentation Based on Non-local Context Guided 3D U-Net.- Deep Small Bowel Segmentation with Cylindrical Topological Constraints.- Learning Sample-adaptive Intensity Lookup Table for Brain Tumor Segmentation.- Superpixel-Guided Label Softening for Medical Image Segmentation.- Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation.- Robust Medical Image Segmentation from Non-expert Annotations with Tri-network.- Robust Fusion of Probability Maps.- Calibrated Surrogate Maximization of Dice.- Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices.- Widening the focus: biomedical image segmentation challenges and the underestimated role of patch sampling and inference strategies.- Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data.- Unsupervised Learning for CT Image Segmentation via Adversarial Redrawing.- Deep Active Contour Network for Medical Image Segmentation.- Learning Crisp Edge Detector Using Logical Refinement Network.- Defending Deep Learning-based Biomedical Image Segmentation from Adversarial Attacks: A Low-cost Frequency Refinement Approach.- CNN-GCN Aggregation Enabled Boundary Regression for Biomedical Image Segmentation.- KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations.- LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation.- INSIDE: Steering Spatial Attention with Non-Imaging Information in CNNs.- SiamParseNet: Joint Body Parsing and Label Propagation in Infant Movement Videos.- Orchestrating Medical Image Compression and Remote Segmentation Networks.- Bounding Maps for Universal Lesion Detection.- Multimodal Priors Guided Segmentation of Liver Lesions in MRI Using Mutual Information Based Graph Co-Attention Networks.- Mt-UcGAN: Multi-task uncertainty-constrained GAN for joint segmentation, quantification and uncertainty estimation of renal tumors on CT.- Weakly Supervised Deep Learning for Breast Cancer Segmentation with Coarse Annotations.- Multi-phase and Multi-level Selective Feature Fusion for Automated Pancreas Segmentation from CT Images.- Asymmetrical Multi-Task Attention U-Net for the Segmentation of Prostate Bed in CT Image.- Learning High-Resolution and Efficient Non-local Features for Brain Glioma Segmentation in MR Images.- Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase  Partially-Annotated CT Scans.- Generation of Annotated Brain Tumor MRIs with Tumor-induced Tissue Deformations for Training and Assessment of Neural Networks.- E2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans.- Universal loss reweighting to balance lesion size inequality in 3D medical image segmentation.- Brain tumor segmentation with missing modalities via latent multi-source correlation representation.- Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices.- Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI.- AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes.- One Click Lesion RECIST Measurement and Segmentation on CT Scans.- Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI.- Deep Attentive Panoptic Model for Prostate Cancer Detection Using Biparametric MRI Scans.- Shape Models and Landmark Detection.- Graph Reasoning and Shape Constraints for Cardiac Segmentation in Congenital Heart Defect.- Nonlinear Regression on Manifolds for Shape Analysis using Intrinsic Bézier Splines.- Self-Supervised Discovery of Anatomical Shape Landmarks.- Shape Mask Generator: Learning to Refine Shape Priors for Segmenting Overlapping Cervical Cytoplasms.- Prostate motion modelling using biomechanically-trained deep neural networks on unstructured nodes.- Deep Learning Assisted Automatic Intra-operative 3D Aortic Deformation Reconstruction.- Landmarks Detection with Anatomical Constraints for Total Hip Arthroplasty Preoperative Measurements.- Instantiation-Net: 3D Mesh Reconstruction from Single 2D Image for Right Ventricle.- Miss the point: Targeted adversarial attack on multiple landmark detection.- Automatic Tooth Segmentation and Dense Correspondence of 3D Dental Model.- Move over there: One-click deformation correction for image fusion during endovascular aortic repair.- Non-Rigid Volume to Surface Registration using a Data-Driven Biomechanical Model.- Deformation Aware Augmented Reality for Craniotomy using 3D/2D Non-rigid Registration of Cortical Vessels.- Skip-StyleGAN: Skip-connected Generative Adversarial Networks for Generating 3D Rendered Image of Hand Bone Complex.- Dynamic multi-object Gaussian process models.- A kernelized multi-level localization method for flexible shape modeling with few training data.- Unsupervised Learning and Statistical Shape Modeling of the Morphometry and Hemodynamics of Coarctation of the Aorta.- Convolutional Bayesian Models for Anatomical Landmarking on Multi-Dimensional Shapes.- SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation.- Multi-Task Dynamic Transformer Network for Concurrent Bone Segmentation and Large-Scale Landmark Localization with Dental CBCT.- Automatic Localization of Landmarks in Craniomaxillofacial CBCT Images using a Local Attention-based Graph Convolution Network.