Medical Image Computing and Computer Assisted Intervention – MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI(12266 Lecture Notes in Computer Science)

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

Table of Contents:
Angiography and Vessel Analysis.- Lightweight Double Attention-fused Networks for Intraoperative Stent Segmentation.- TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling.- Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction.- Branch-aware Double DQN for Centerline Extraction in Coronary CT Angiography.- Automatic CAD-RADS Scoring from CCTA Scans using Deep Learning.- Higher-Order Flux with Spherical Harmonics Transform for Vascular Analysis.- Cerebrovascular Segmentation in MRA via Reverse Edge Attention Network.- Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement.- Time matters: Handling spatio-temporal perfusion information for automated TICI scoring.- ID-Fit: Intra-saccular Device adjustment for personalized cerebral aneurysm treatment.- JointVesselNet: Joint Volume-Projection Convolutional Embedding Networks for 3D Cerebrovascular Segmentation.- Classification of Retinal Vessels into Artery-Vein in OCT Angiography Guided by Fundus Images.- Vascular surface segmentation for intracranial aneurysm isolation and quantification.- Breast Imaging.- Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities.- 2D X-ray mammography and 3D breast MRI registration.- A Second-order Subregion Pooling Network for Breast Ultrasound Lesion Segmentation.- Multi-Scale Gradational-Order Fusion Framework for Breast lesions Classification Using Ultrasound images.- Computer-aided Tumor Diagnosis in Automated Breast Ultrasound using 3D Detection Network.- Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound.- MommiNet: Mammographic Multi-View Mass Identification Networks.- Multi-Site Evaluation of a Study-Level Classifier for Mammography using Deep Learning.- The case of missed cancers: Applying AI as a radiologist’s safety net.- Decoupling Inherent Risk and Early Cancer Signs in Image-based Breast Cancer Risk Models.- Multi-task learning for detection and classification of cancer in screening mammography.- Colonoscopy.- Adaptive Context Selection for Polyp Segmentation.- PraNet: Parallel Reverse Attention Network for Polyp Segmentation.- Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy.- PolypSeg: an Efficient Context-aware Network for Polyp Segmentation from Colonoscopy Videos.- Endoscopic polyp segmentation using a hybrid 2D/3D CNN.- Dermatology.- A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images.- Fairness of Classifiers Across Skin Tones in Dermatology.- Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification.- Clinical-Inspired Network for Skin Lesion Recognition.- Multi-class Skin Lesion Segmentation for Cutaneous T-cell Lymphomas on High-Resolution Clinical Images.- Fetal Imaging.- Deep learning automatic fetal structures segmentation in MRI scans with few annotated datasets.- Data-Driven Multi-Contrast Spectral Microstructure Imaging with InSpect.- Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency.- Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy.- Automatic angle of progress measurement of intrapartum transperineal ultrasound image with deep learning.- Joint Image Quality Assessment and Brain Extraction of Fetal MRI using Deep Learning.- Heart and Lung Imaging.- Accelerated 4D Respiratory Motion-resolved Cardiac MRI with a Model-based Variational Network.- Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation.- ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation.- A Bottom-up Approach for Real-time Mitral Valve Annulus Modeling on 3D Echo Images.- A Semi-supervised Joint Network for Simultaneous Left Ventricular Motion Tracking andSegmentation in 4D Echocardiography.- Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets.- Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction.- Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study.- Learning Tumor Growth via Follow-Up Volume Prediction for Lung Nodules.- Multi-stream Progressive Up-sampling Network for Dense CT Image Reconstruction.- Abnormality Detection in Chest X-ray Images Using Uncertainty Prediction Autoencoders.- Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification.- CPM-Net: A 3D Center-Points Matching Network for Pulmonary Nodule Detection in CT Scans.- Interpretable Identification of Interstitial Lung Diseases (ILD) Associated Findings from CT.- Learning with Sure Data for Nodule-Level Lung Cancer Prediction.- Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation.- Class-Aware Multi-Window Adversarial Lung Nodule Synthesis Conditioned on Semantic Features.- Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation.- Deep Active Learning for Effective Pulmonary Nodule Detection.- Musculoskeletal Imaging.- Towards Robust Bone Age Assessment: Rethinking Label Noise and Ambiguity.- Improve bone age assessment by learning from anatomical local regions.- An Analysis by Synthesis Method that Allows Accurate Spatial Modeling of Thickness of Cortical Bone from Clinical QCT.- Segmentation of Paraspinal Muscles at Varied Lumbar Spinal Levels by Explicit Saliency-Aware Learning.- Manifold Ordinal-Mixup for Ordered Classes inTW3-based Bone Age Assessment.- Contour-based Bone Axis Detection for X-Ray Guided Surgery on the Knee.- Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks.- Discriminative dictionary-embedded network for comprehensivevertebrae tumor diagnosis.- Multi-vertebrae segmentation from arbitrary spine MR images under global view.- A Convolutional Approach to Vertebrae Identification in Whole Spine MRI.- Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification.- Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection.- 3D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CT.- SIMBA: Specific Identity Markers for Bone Age Assessment.- Doctor Imitator: A Graph-based Bone Age Assessment Framework Using Hand Radiographs.- Inferring the 3D Standing Spine Posture from 2D Radiographs.- Generative Modelling of 3D in-silico Spongiosa with Controllable Micro-Structural Parameters.- GAN-based Realistic Bone Ultrasound Image and Label Synthesis for Improved Segmentation.- Robust Bone Shadow Segmentation from 2D Ultrasound Through Task Decomposition.