Description
The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning – self-supervised learning; machine learning – semi-supervised learning; and machine learning – weakly supervised learning Part III: machine learning – advances in machine learning theory; machine learning – attention models; machine learning – domain adaptation; machine learning – federated learning; machine learning – interpretability / explainability; and machine learning – uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications – cardiac; and clinical applications – vascular Part VII: clinical applications – abdomen; clinical applications – breast; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – neuroimaging – brain development; clinical applications – neuroimaging – DWI and tractography; clinical applications – neuroimaging – functional brain networks; clinical applications – neuroimaging – others; and clinical applications – oncology Part VIII: clinical applications – ophthalmology; computational (integrative) pathology; modalities – microscopy; modalities – histopathology; and modalities – ultrasound *The conference was held virtually. Image Reconstruction.- Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images.- Over-and-Under Complete Convolutional RNN for MRI Reconstruction.- TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translation.- Synthesizing Multi-Tracer PET Images for Alzheimer’s Disease Patients using a 3D Unified Anatomy-aware Cyclic Adversarial Network.- Generalised Super Resolution for Quantitative MRI Using Self-Supervised Mixture of Experts.- TransCT: Dual-path Transformer for Low Dose Computed Tomography.- IREM: High-Resolution Magnetic Resonance Image Reconstruction via Implicit Neural Representation.- DA-VSR: Domain Adaptable Volumetric Super-Resolution For Medical Images.- Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation.- Fast Magnetic Resonance Imaging on Regions of Interest: From Sensing to Reconstruction.- InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction.- Depth Estimation for Colonoscopy Images with Self-supervised Learning from Videos.- Joint Optimization of Hadamard Sensing and Reconstruction in Compressed Sensing Fluorescence Microscopy.- Multi-Contrast MRI Super-Resolution via a Multi-Stage Integration Network.- Generator Versus Segmentor: Pseudo-healthy Synthesis.- Real-Time Mapping of Tissue Properties for Magnetic Resonance Fingerprinting.- Estimation of High Frame Rate Digital Subtraction Angiography Sequences at Low Radiation Dose.- RLP-Net: Recursive Light Propagation Network for 3-D Virtual Refocusing.- Noise Mapping and Removal in Complex-Valued Multi-Channel MRI via Optimal Shrinkage of Singular Values.- Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction.- Universal Undersampled MRI Reconstruction.- A Neural Framework for Multi-Variable Lesion Quantification Through B-mode Style Transfer.- Temporal Feature Fusion with Sampling Pattern Optimization for Multi-echo Gradient Echo Acquisition and Image Reconstruction.- Dual-Domain Adaptive-Scaling Non-Local Network for CT Metal Artifact Reduction.- Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution.- Adaptive Squeeze-and-Shrink Image Denoising for Improving Deep Detection of Cerebral Microbleeds.- 3D Transformer-GAN for High-quality PET Reconstruction.- Learnable Multi-scale Fourier Interpolation for Sparse View CT Image Reconstruction.- U-DuDoNet: Unpaired dual-domain network for CT metal artifact reduction.- Task Transformer Network for Joint MRI Reconstruction and Super-Resolution.- Conditional GAN with an Attention-based Generator and a 3D Discriminator for 3D Medical Image Generation.- Multimodal MRI Acceleration via Deep Cascading Networks with Peer-layer-wise Dense Connections.- Rician noise estimation for 3D Magnetic Resonance Images based on Benford’s Law.- Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization.- Label-Free Physics-Informed Image Sequence Reconstruction with Disentangled Spatial-Temporal Modeling.- High-Resolution Hierarchical Adversarial Learning for OCT Speckle Noise Reduction.- Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Framework.- Acceleration by deep-learnt sharing of superfluous information in multi-contrast MRI.- Sequential Lung Nodule Synthesis using Attribute-guided Generative Adversarial Networks.- A Data-driven Approach for High Frame Rate Synthetic Transmit Aperture Ultrasound Imaging.- Interpretable deep learning for multimodal super-resolution of medical images.- MRI Super-Resolution Through Generative Degradation Learning.- Task-Oriented Low-Dose CT Image Denoising.- Revisiting contour-driven and knowledge-based deformable models: application to 2D-3D proximal femur reconstruction from X-ray images.- Memory-efficient Learning for High-dimensional MRI Reconstruction.- SA-GAN: Structure-Aware GAN for Organ-Preserving Synthetic CT Generation.- Clinical Applications – Cardiac.- Distortion Energy for Deep Learning-based Volumetric Finite Element Mesh Generation for Aortic Valves.- Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation.- EchoCP: An Echocardiography Dataset in Contrast Transthoracic Echocardiography for Patent Foramen Ovale Diagnosis.- Transformer Network for Significant Stenosis Detection in CCTA of Coronary Arteries.- Training Automatic View Planner for Cardiac MR Imaging via Self-Supervision by Spatial Relationship between Views.- Phase-independent Latent Representation for Cardiac Shape Analysis.- Cardiac Transmembrane Potential Imaging with GCN Based Iterative Soft Threshold Network.- AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs.- TVnet: Automated Time-Resolved Tracking of the Tricuspid Valve Plane in MRI Long-Axis Cine Images with a Dual-Stage Deep Learning Pipeline.- Clinical Applications – Vascular.- Deep Open Snake Tracker for Vessel Tracing.- MASC-Units: Training Oriented Filters for Segmenting Curvilinear Structures.- Vessel Width Estimation via Convolutional Regression.- Renal Cell Carcinoma Classification from Vascular Morphology.
