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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 r...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: de Bruijne, Marleen (Editor), Cattin, Philippe C. (Editor), Cotin, Stéphane (Editor), Padoy, Nicolas (Editor), Speidel, Stefanie (Editor), Zheng, Yefeng (Editor), Essert, Caroline (Editor)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2021.
Edition:1st ed. 2021.
Series:Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12901
Subjects:
Optical data processing.
Artificial intelligence.
Computers.
Bioinformatics.
Pattern recognition.
Image Processing and Computer Vision.
Artificial Intelligence.
Information Systems and Communication Service.
Computational Biology/Bioinformatics.
Pattern Recognition.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • 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 Balancing Attention 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.