Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images: First Challenge, MyoPS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings

Zhuang, Xiahai and Li, Lei (eds.) (2020) Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images: First Challenge, MyoPS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12554), vol. 12554, Springer Cham, 177pp.

Abstract

The proceedings contain 16 papers. The special focus in this conference is on Myocardial Pathology Segmentation Combining Multi-Sequence CMR Challenge. The topics include: Two-Stage Method for Segmentation of the Myocardial Scars and Edema on Multi-sequence Cardiac Magnetic Resonance; efficientSeg: A Simple But Efficient Solution to Myocardial Pathology Segmentation Challenge; Recognition and Standardization of Cardiac MRI Orientation via Multi-tasking Learning and Deep Neural Networks; Cascaded Framework with Complementary CMR Information for Myocardial Pathology Segmentation; Dual-Path Feature Aggregation Network Combined Multi-layer Fusion for Myocardial Pathology Segmentation with Multi-sequence Cardiac MR; stacked and Parallel U-Nets with Multi-output for Myocardial Pathology Segmentation; accurate Myocardial Pathology Segmentation with Residual U-Net; Dual Attention U-Net for Multi-sequence Cardiac MR Images Segmentation; Automatic Myocardial Scar Segmentation from Multi-sequence Cardiac MRI Using Fully Convolutional Densenet with Inception and Squeeze-Excitation Module; exploring Ensemble Applications for Multi-sequence Myocardial Pathology Segmentation; preface; Stacked BCDU-Net with Semantic CMR Synthesis: Application to Myocardial Pathology Segmentation Challenge; CMS-UNet: Cardiac Multi-task Segmentation in MRI with a U-Shaped Network; Fully Automated Deep Learning Based Segmentation of Normal, Infarcted and Edema Regions from Multiple Cardiac MRI Sequences.

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Contributors

Editor: Lei Li

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