Segmentation, classification, and registration of multi-modality medical imaging data : MICCAI 2020 challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings.

Intro
ABCs 2020 Preface
Organization
L2R 2020 Preface
Organization
TN-SCUI 2020 Preface
Organization
Contents
ABCs
Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images
Cross-Modality Brain Structures Image Segmentation for the Radiotherapy Target Definition and Plan Optimization
1 Introduction
2 Challenge Setup
2.1 Imaging
2.2 Structure Labeling
2.3 Data Pre-processing
2.4 Segmentation Accuracy Evaluation and Scoring
2.5 Organization
3 Results
3.1 Inter-Rater Variability Study

3.2 Performance of the Algorithms
4 Summary and Conclusion
References
Domain Knowledge Driven Multi-modal Segmentation of Anatomical Brain Barriers to Cancer Spread
1 Introduction
2 Methods
2.1 Data Description
2.2 Network Architecture
2.3 Label Merging for Symmetric Structures
2.4 Multi-modality Ensemble
2.5 Training Protocol
3 Results
4 Discussion
5 Conclusion
References
Ensembled ResUnet for Anatomical Brain Barriers Segmentation
1 Introduction
2 Method
2.1 Encoder Design
2.2 Decoder Design
2.3 Loss Function

2.4 Pseudo Training with Model Ensemble
2.5 Optimization
3 Experiments
3.1 Implementation Details and Data Processing
3.2 Quantitative and Qualitative Analysis
4 Conclusions
References
An Enhanced Coarse-to-Fine Framework for the Segmentation of Clinical Target Volume
1 Introduction
2 Method
2.1 F-Loss to Keep the High Recall Rate of Coarse Segmentation
2.2 Iterative Refinement to Iteratively Refine the Results
2.3 Ensemble Refinement to Fuse Multiple Information to Get Finer Results
3 Experiments
3.1 DataSet
3.2 Training Details
4 Results