Medical Image Computing and Computer Assisted Intervention - MICCAI 2021: 24th International Conference, Strasbourg, France, September 27-October 1, 2

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 Appl