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

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.