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

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

Brain Development and Atlases.- A New Metric for Characterizing Dynamic Redundancy of Dense Brain Chronnectome and Its Application to Early Detection of Alzheimer's Disease.- A computational framework for dissociating development-related from individually variable flexibility in regional modularity assignment in early infancy.- Domain-invariant Prior Knowledge Guided Attention Networks for Robust Skull Stripping of Developing Macaque Brains.- Parkinson's Disease Detection from fMRI-derived Brainstem Regional Functional Connectivity Networks.- Persistent Feature Analysis of Multimodal Brain Networks Using Generalized Fused Lasso for EMCI Identification.- Recovering Brain Structural Connectivity from Functional Connectivity via Multi-GCN based Generative Adversarial Network.- From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts from Resting-state Functional Connectivity.- Disentangled Intensive Triplet Autoencoder for Infant Functional Connectome Fingerprinting.- COVLET: Covariance-based Wavelet-like Transform for Statistical Analysis of Brain Characteristics in Children.- Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-Task Regression.- Self-weighted Multi-Task Learning for Subjective Cognitive Decline Diagnosis.- Unified Brain Network with Functional and Structural Data.- Integrating Similarity Awareness and Adaptive Calibration in Graph Convolution Network to Predict Disease.- Infant Cognitive Scores Prediction With Multi-stream Attention-based Temporal Path Signature Features.- Masked Multi-Task Network for Case-level Intracranial Hemorrhage Classification in Brain CT Volumes.- Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates.- Supervised Multi-topology Network Cross-diffusion for Population-Driven Brain Network Atlas Estimation.- Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast.- BDB-Net: Boundary-enhanced Dual Branch Network for Whole Brain Segmentation.- Brain Age Estimation From MRI Using a Two-Stage Cascade Network with a Ranking Loss.- Context-Aware Refinement Network Incorporating Structural Connectivity Prior for Brain Midline Delineation.- Optimizing Visual Cortex Parameterization with Error-Tolerant Teichmüller Map in Retinotopic Mapping.- Multi-Scale Enhanced Graph Convolutional Network for Early Mild Cognitive Impairment Detection.- Construction of Spatiotemporal Infant Cortical Surface Functional Templates.- DWI and Tractography.- Tract Dictionary Learning for Fast and Robust Recognition of Fiber Bundles.- Globally Optimized Super-Resolution of Diffusion MRI Data via Fiber Continuity.- White Matter Tract Segmentation with Self-supervised Learning.- Estimating Tissue Microstructure with Undersampled Diffusion Data via Graph Convolutional Neural Networks.- Tractogram filtering of anatomically non-plausible fibers with geometric deep learning.- Unsupervised Deep Learning for Susceptibility Distortion Correction in Connectome Imaging.- Hierarchical geodesic modeling on the diffusion orientation distribution function for longitudinal DW-MRI analysis.- TRAKO: Efficient Transmission of Tractography Data for Visualization.- Spatial Semantic-Preserving Latent Space Learning for Accelerated DWI Diagnostic Report Generation.- Trajectories from Distribution-valued Functional Curves: A Unified Wasserstein Framework.- Characterizing Intra-Soma Diffusion with Spherical Mean Spectrum Imaging.- Functional Brain Networks.- Estimating Common Harmonic Waves of Brain Networks on Stiefel Manifold.- Neural Architecture Search for Optimization of Spatial-temporal Brain Network Decomposition.- Attention-Guided Deep Graph Neural Network for Longitudinal Alzheimer’s Disease Analysis.- Enriched Representation Learning in Resting-State fMRI for Early MCI Diagnosis.- Whole MILC: generalizing learned dynamics across tasks, datasets, and populations.- A physics-informed geometric learning model for pathological tau spread in Alzheimer's disease.- A deep pattern recognition approach for inferring respiratory volume fluctuations from fMRI data.- A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism.- Poincare embedding reveals edge-based functional networks of the brain.- The constrained network-based statistic: a new level of inference for neuroimaging.- Learning Personal Representations from fMRIby Predicting Neurofeedback Performance.- A 3D Convolutional Encapsulated Long Short-Term Memory (3DConv-LSTM) Model for Denoising fMRI Data.- Detecting Changes of Functional Connectivity by Dynamic Graph Embedding Learning.- Discovering Functional Brain Networks with 3D Residual Autoencoder (ResAE).- Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classification.- Global Diffeomorphic Phase Alignment of Time-series from Resting-state fMRI Data.- Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis.- A shared neural encoding model for the prediction of subject-specific fMRI response.- Neuroimaging.- Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain Graph.- Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction.- Fisher-Rao Regularized Transport Analysis of the Glymphatic System and Waste Drainage.- Joint Neuroimage Synthesis and Representation Learning for Conversion Prediction of Subjective Cognitive Decline.- Differentiable Deconvolution for Improved Stroke Perfusion Analysis.- Spatial Similarity-Aware Learning and Fused Deep Polynomial Network for Detection of Obsessive-Compulsive Disorder.- Deep Representation Learning For Multimodal Brain Networks.- Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis.- Patch-based abnormality maps for improved deep learning-based classification of Huntington's disease.- A Deep Spatial Context Guided Framework for Infant Brain Subcortical Segmentation.- Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE.- Spatial Component Analysis to Mitigate Multiple Testing in Voxel-Based Analysis.- MAGIC: Multi-scale Heterogeneity Analysis and Clustering for Brain Diseases.- PIANO: Perfusion Imaging via Advection-diffusion.- Hierarchical Bayesian Regression for Multi-Site Normative Modeling of Neuroimaging Data.- Image-level Harmonization of Multi-Site Data using Image-and-Spatial Transformer Networks.- A Disentangled Latent Space for Cross-Site MRI Harmonization.- Automated Acquisition Planning for Magnetic Resonance Spectroscopy in Brain Cancer.- Positron Emission Tomography.- Simultaneous Denoising and Motion Estimation for Low-dose Gated PET using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning.- Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy.- Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search.- Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network.- Rethinking PET Image Reconstruction: Ultra-Low-Dose, Sinogram and Deep Learning.- Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network.- Collimatorless Scintigraphy for Imaging Extremely Low Activity Targeted Alpha Therapy (TAT) with Weighted Robust Least Square (WRLS).