Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images: First Challenge, Myops 2020, Held in Conjunction with Mi

ISBN-13: 9783030656508 / Angielski / Miękka / 2020 / 177 str.

Xiahai Zhuang; Lei Li

Spis treści

Stacked BCDU-net with semantic CMR synthesis: application to Myocardial PathologySegmentation challenge.- EfficientSeg: A Simple but Efficient Solution to Myocardial Pathology Segmentation Challenge.- Two-stage Method for Segmentation of the Myocardial Scars and Edema on Multi-sequence Cardiac Magnetic Resonance.- Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images.- Myocardial Edema and Scar Segmentation using a Coarse-to-Fine Framework with Weighted Ensemble.- Exploring ensemble applications for multi-sequence myocardial pathology segmentation.- Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling.- Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences.- CMS-UNet: Cardiac Multi-task Segmentation in MRI with a U-shaped Network.- Automatic Myocardial Scar Segmentation from Multi-Sequence Cardiac MRI using Fully Convolutional Densenet with Inception and Squeeze-Excitation Module.- Dual Attention U-net for Multi-Sequence Cardiac MR Images Segmentation.- Accurate Myocardial Pathology Segmentation with Residual U-Net.- Stacked and Parallel U-Nets with Multi-Output for Myocardial Pathology Segmentation.- Dual-path Feature Aggregation Network Combined Multi-layer Fusion for Myocardial Pathology Segmentation with Multi-sequence Cardiac MR.- Cascaded Framework with Complementary CMR Information for Myocardial Pathology Segmentation.- CMRadjustNet: Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks.