Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 [electronic resource] : 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VII /

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 DualBranch 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 Fusionfor 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).

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.