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

• Main Authors: de Bruijne, Marleen, Cattin, Philippe C, Cotin, Stéphane, Padoy, Nicolas, Speidel, Stefanie, Zheng, Yefeng, Essert, Caroline
• Format: eBook Conference Proceeding Book
• Language: English
• Published: Cham Springer International Publishing AG 2021; Springer International Publishing; Springer Nature
• Edition: 1
• Series: Lecture Notes in Computer Science
• Subjects: Artificial Intelligence; Computational Biology/Bioinformatics; Computer Science; Diagnostic imaging-Data processing; Image Processing and Computer Vision; Information Systems and Communication Service; Modeling and Simulation; Optical data processing; Pattern Recognition

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 542 re...

Table of Contents:

• 3.4 Objective Function for Learning the Segmentation Network
• References -- Multi-task, Multi-domain Deep Segmentation with Shared Representations and Contrastive Regularization for Sparse Pediatric Datasets -- 1 Introduction -- 2 Method -- 2.1 Deep Segmentation Model with Domain-Specific Layers (DSL) -- 2.2 Supervised Contrastive Regularization -- 3 Experiments -- 3.1 Imaging Datasets -- 3.2 Implementation Details -- 3.3 Evaluation of Predicted Segmentation -- 4 Results and Discussion -- 4.1 Segmentation Results -- 4.2 Supervised Contrastive Regularization Visualization -- 5 Conclusion -- References -- TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations -- 1 Introduction -- 2 Method -- 2.1 Experimental Setup -- 3 Results and Discussion -- 4 Conclusion -- References -- Learning Consistency- and Discrepancy-Context for 2D Organ Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Consistency Context-Based Organ Segmentation -- 2.2 Segmentation Refinement with Discrepancy Context Knowledge -- 3 Datasets and Implementation Details -- 4 Experimental Results and Analysis -- 4.1 The Effectiveness of Our Proposed Method -- 4.2 Portability with Different Segmentation Models -- 4.3 Qualitative Results -- 5 Conclusion -- References -- Partially-Supervised Learning for Vessel Segmentation in Ocular Images -- 1 Introduction -- 2 Method -- 2.1 Partially-Supervised Learning -- 2.2 Active Learning Framework -- 2.3 Latent MixUp -- 2.4 Loss Function -- 3 Experiment -- 3.1 Experiment Setting -- 3.2 Performance Comparison -- 3.3 Ablation Studies -- 4 Conclusion -- References -- Unsupervised Network Learning for Cell Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Formulation of the Unsupervised Segmentation Problem -- 3.2 USAR for Unsupervised Segmentation Network Learning -- 3.3 Avoid Trivial Solutions in Unsupervised Network Learning
• 3.5 Ablation Study -- 4 Discussion -- 5 Conclusion -- References -- Patch-Free 3D Medical Image Segmentation Driven by Super-Resolution Technique and Self-Supervised Guidance -- 1 Introduction -- 2 Methodology -- 2.1 Multi-task Learning -- 2.2 Self-Supervised Guidance Module -- 2.3 Task-Fusion Module -- 2.4 Overall Objective Function -- 3 Experiments -- 3.1 Datasets -- 3.2 Implementation Details -- 3.3 Ablation Study -- 3.4 Experimental Results -- 4 Conclusion -- References -- Progressively Normalized Self-Attention Network for Video Polyp Segmentation -- 1 Introduction -- 2 Method -- 2.1 Normalized Self-attention (NS) -- 2.2 Progressive Learning Strategy -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Evaluation on Video Polyp Segmentation -- 3.3 Ablation Study -- 4 Conclusion -- References -- SGNet: Structure-Aware Graph-Based Network for Airway Semantic Segmentation -- 1 Introduction -- 2 Method -- 2.1 Multi-task U-Net Module -- 2.2 Structure-Aware Graph Convolutional Network -- 2.3 Loss Functions and Training Methodology -- 3 Experiments and Results -- 3.1 Datasets and Implementation Details -- 3.2 Results -- 4 Conclusion -- References -- NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale -- 1 Introduction -- 1.1 Related Works -- 2 NucMM Dataset -- 3 Method -- 3.1 Hybrid-Representation Learning -- 3.2 Instance Decoding -- 3.3 Implementation -- 4 Experiments -- 4.1 Methods in Comparison -- 4.2 Benchmark Results on the NucMM Dataset -- 4.3 Sensitivity of the Decoding Parameters -- 5 Conclusion -- References -- AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions -- 1 Introduction -- 1.1 Related Works -- 2 AxonEM Dataset -- 2.1 Dataset Description -- 2.2 Dataset Annotation -- 2.3 Dataset Analysis -- 3 Methods -- 3.1 Task and Evaluation Metric -- 3.2 State-of-the-Art Methods -- 4 Experiments
• Intro -- Preface -- Organization -- Contents - Part I -- Image Segmentation -- Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Materials -- 2.2 Hepatic CT Preprocessing -- 2.3 Mean-Teacher-assisted Confident Learning Framework -- 2.4 Loss Function -- 3 Experiments and Results -- 4 Conclusion -- References -- TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation -- 1 Introduction -- 2 Proposed Method -- 3 Experiments and Results -- 4 Conclusion -- References -- Pancreas CT Segmentation by Predictive Phenotyping -- 1 Introduction -- 2 Method -- 2.1 Problem Formulation -- 2.2 Loss Functions -- 2.3 Phenotype Embedding -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Comparison with State-of-the-Arts -- 3.4 Results -- 4 Discussion and Conclusion -- References -- Medical Transformer: Gated Axial-Attention for Medical Image Segmentation -- 1 Introduction -- 2 Medical Transformer (MedT) -- 2.1 Self-attention Overview -- 2.2 Gated Axial-Attention -- 2.3 Local-Global Training -- 3 Experiments and Results -- 3.1 Dataset Details -- 3.2 Implementation Details -- 3.3 Results -- 4 Conclusion -- References -- Anatomy-Constrained Contrastive Learning for Synthetic Segmentation Without Ground-Truth -- 1 Introduction -- 2 Methods -- 3 Experimental Results -- 4 Discussion and Conclusion -- References -- Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels -- 1 Introduction -- 2 Methodology -- 2.1 Model Structure -- 2.2 Study Group Learning (SGL) Scheme -- 2.3 Vessel Label Erasing -- 3 Experiments -- 3.1 Dataset and Implementation -- 3.2 Learned Retinal Image Enhancement -- 3.3 Study Group Learning -- 4 Conclusions -- References
• Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting -- 1 Introduction -- 2 Method -- 2.1 Spatial Aggregation Module -- 2.2 Uncertain Region Inpainting Module -- 2.3 Loss Function and Training Strategy -- 3 Materials and Experiments -- 4 Discussion and Conclusions -- References -- Convolution-Free Medical Image Segmentation Using Transformers -- 1 Introduction -- 2 Materials and Methods -- 2.1 Proposed Network -- 2.2 Implementation -- 3 Results and Discussion -- 4 Conclusions -- References -- Consistent Segmentation of Longitudinal Brain MR Images with Spatio-Temporal Constrained Networks -- 1 Introduction -- 2 Method -- 2.1 Dataset -- 2.2 Spatio-Temporal Constrained Deep Learning Model -- 2.3 Initial ROI Segmentation with Supervised Learning -- 2.4 Consistent Segmentation with Semi-supervised Learning -- 2.5 Implement Details -- 3 Experiments and Results -- 4 Conclusion -- References -- A Multi-branch Hybrid Transformer Network for Corneal Endothelial Cell Segmentation -- 1 Introduction -- 2 Method -- 2.1 Residual Transformer Block -- 2.2 The Body, Edge, and Final Branches -- 2.3 Loss Function -- 3 Experiments -- 3.1 Datasets and implementation Details -- 3.2 Comparison with SOTA methods -- 3.3 Ablation Study -- 4 Conclusion -- References -- TransBTS: Multimodal Brain Tumor Segmentation Using Transformer -- 1 Introduction -- 2 Method -- 2.1 Overall Architecture of TransBTS -- 2.2 Network Encoder -- 2.3 Network Decoder -- 3 Experiments -- 3.1 Main Results -- 3.2 Model Complexity -- 3.3 Ablation Study -- 4 Conclusion -- References -- Automatic Polyp Segmentation via Multi-scale Subtraction Network -- 1 Introduction -- 2 Method -- 2.1 Multi-scale Subtraction Module -- 2.2 LossNet -- 3 Experiments -- 3.1 Datasets -- 3.2 Evaluation Metrics -- 3.3 Implementation Details -- 3.4 Comparisons with State-of-the-art
• 4.1 Implementation Details -- 4.2 Benchmark Results on SNEMI3D Dataset -- 4.3 Benchmark Results on AxonEM Dataset -- 5 Conclusion -- References -- Improved Brain Lesion Segmentation with Anatomical Priors from Healthy Subjects -- 1 Introduction -- 2 Method -- 2.1 Problem Formulation -- 2.2 Intensity-Based Strategy -- 2.3 Distribution-Based Strategy -- 2.4 Integration with CNNs and Implementation Details -- 3 Results -- 3.1 Dataset Description -- 3.2 Experimental Settings -- 3.3 Segmentation Performance -- 4 Conclusion -- References -- CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation -- 1 Introduction -- 2 Method -- 2.1 Problem Formulation -- 2.2 CarveMix -- 2.3 Relationship with Mixup and CutMix -- 2.4 Implementation Details -- 3 Experiments -- 3.1 Data Description -- 3.2 Evaluation Results -- 4 Conclusion -- References -- Boundary-Aware Transformers for Skin Lesion Segmentation -- 1 Introduction -- 2 Method -- 2.1 Basic Transformer for Segmentation -- 2.2 Boundary-Aware Transformer -- 2.3 Objective Function -- 3 Experimental Results -- 3.1 Datasets -- 3.2 Implementation Details -- 3.3 Comparison with State-of-the-Arts -- 3.4 Ablation Study -- 4 Conclusion -- References -- A Topological-Attention ConvLSTM Network and Its Application to EM Images -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Spatial Topological-Attention (STA) Module -- 3.2 Iterative Topological-Attention (ITA) Module -- 4 Experiments -- 5 Conclusion -- References -- BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation -- 1 Introduction -- 2 Methods -- 2.1 BiO-Net++: A Multi-scale Upgrade of BiO-Net -- 2.2 BiX-NAS: Hierarchical Search for Efficient BiO-Net++ -- 2.3 Analysis of Searching Fairness and Deficiency -- 3 Experiments -- 3.1 Datasets and Implementation Details -- 3.2 Experimental Results -- 4 Conclusion