Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I

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 Segmentation.- Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation.- TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation.- Pancreas CT Segmentation by Predictive Phenotyping.- Medical Transformer: Gated Axial-Attention for Medical Image Segmentation.- Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth.- Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels.- Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting.- Convolution-Free Medical Image Segmentation using Transformer Networks.- Consistent Segmentation of Longitudinal Brain MR Images with Spatio-Temporal Constrained Networks.- A Multi-Branch Hybrid Transformer Network for Corneal Endothelial Cell Segmentation.- TransBTS: Multimodal Brain Tumor Segmentation Using Transformer.- Automatic Polyp Segmentation via Multi-scale Subtraction Network.- Patch-Free 3D Medical Image Segmentation Driven by Super-Resolution Technique and Self-Supervised Guidance.- Progressively Normalized Self-Attention Network for Video Polyp Segmentation.- SGNet: Structure-aware Graph-based Network for Airway Semantic Segmentation.- NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale.- AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions.- Improved Brain Lesion Segmentation with Anatomical Priors from Healthy Subjects.- CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation.- Boundary-aware Transformers for Skin Lesion Segmentation.- A Topological-Attention ConvLSTM Network and Its Application to EM Images.- BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation.- Multi-Task, Multi-Domain Deep Segmentation with Shared Representations and Contrastive Regularization for Sparse Pediatric Datasets.- TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations.- Learning Consistency- and Discrepancy-Context for 2D Organ Segmentation.- Partial-supervised Learning for Vessel Segmentation in Ocular Images.- Unsupervised Network Learning for Cell Segmentation.- MT-UDA: Towards Unsupervised Cross-Modality Medical Image Segmentation with Limited Source Labels.- Context-aware virtual adversarial training for anatomically-plausible segmentation.- Interactive segmentation via deep learning and B-spline explicit active surfaces.- Multi-Compound Transformer for Accurate Biomedical Image Segmentation.- kCBAC-Net: Deeply Supervised Complete Bipartite Networks with Asymmetric Convolutions for Medical Image Segmentation.- Multi-frame Attention Network for Left Ventricle Segmentation in 3D Echocardiography.- Coarse-to-fine Segmentation of Organs at Risk in Nasopharyngeal Carcinoma Radiotherapy.- Joint Segmentation and Quantification of Main Coronary Vessels Using Dual-branch Multi-scale Attention Network.- A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation.- Comprehensive Importance-based Selective Regularization for Continual Segmentation Across Multiple Sites.- ReSGAN: Intracranial Hemorrhage Segmentation with Residuals of Synthetic Brain CT Scans.- Refined Local-imbalance-based Weight for Airway Segmentation in CT.- Selective Learning from External Data for CT Image Segmentation.- Projective Skip-Connections for Segmentation Along a Subset of Dimensions in Retinal OCT.- MouseGAN: GAN-Based Multiple MRI Modalities Synthesis and Segmentation for Mouse Brain Structures.- Style Curriculum Learning for Robust Medical Image Segmentation.- Towards Efficient Human-Machine Collaboration: Real-Time Correction Effort Prediction for Ultrasound Data Acquisition.- Residual Feedback Network for Breast Lesion Segmentation in Ultrasound Image.- Learning to Address Intra-segment Misclassification in Retinal Imaging.- Flip Learning: Erase to Segment.- DC-Net: Dual Context Network for 2D Medical Image Segmentation.- LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation.- Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation.- A hybrid attention ensemble framework for zonal prostate segmentation.- 3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation.- HRENet: A Hard Region Enhancement Network for Polyp Segmentation.- A Novel Hybrid Convolutional Neural Network for Accurate Organ Segmentation in 3D Head and Neck CT Images.- TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor Segmentation.- Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation.- Hybrid graph convolutional neural networks for anatomical segmentation.- RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans.- Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation.- CCBANet: Cascading Context and BalancingAttention for Polyp Segmentation.- Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation.- TUN-Det: A Novel Network for Thyroid Ultrasound Nodule Detection.- Distilling effective supervision for robust medical image segmentation with noisy labels.- On the relationship between calibrated predictors and unbiased volume estimation.- High-resolution segmentation of lumbar vertebrae from conventional thick slice MRI.- Shallow Attention Network for Polyp Segmentation.- A Line to Align: Deep Dynamic Time Warping for Retinal OCT Segmentation.- Learnable Oriented-Derivative Network for Polyp Segmentation.- LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images.