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ORCIDMyoPS@MICCAI 2020: Lima, Peru
- Xiahai Zhuang
, Lei Li:
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 12554, Springer 2020, ISBN 978-3-030-65650-8 - Carlos Martín-Isla, Maryam Asadi-Aghbolaghi, Polyxeni Gkontra, Víctor M. Campello, Sergio Escalera, Karim Lekadir:
Stacked BCDU-Net with Semantic CMR Synthesis: Application to Myocardial Pathology Segmentation Challenge. 1-16 - Jianpeng Zhang, Yutong Xie, Zhibin Liao, Johan Verjans, Yong Xia:
EfficientSeg: A Simple But Efficient Solution to Myocardial Pathology Segmentation Challenge. 17-25 - Yanfei Liu, Maodan Zhang, Qi Zhan, Dongdong Gu, Guocai Liu:
Two-Stage Method for Segmentation of the Myocardial Scars and Edema on Multi-sequence Cardiac Magnetic Resonance. 26-36 - Zhen Zhang, Chenyu Liu, Wangbin Ding, Sihan Wang, Chenhao Pei, Mingjing Yang, Liqin Huang:
Multi-modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images. 37-48 - Shuwei Zhai, Ran Gu, Wenhui Lei, Guotai Wang:
Myocardial Edema and Scar Segmentation Using a Coarse-to-Fine Framework with Weighted Ensemble. 49-59 - Markus J. Ankenbrand, David Lohr, Laura Maria Schreiber:
Exploring Ensemble Applications for Multi-sequence Myocardial Pathology Segmentation. 60-67 - Haochuan Jiang, Chengjia Wang, Agisilaos Chartsias, Sotirios A. Tsaftaris:
Max-Fusion U-Net for Multi-modal Pathology Segmentation with Attention and Dynamic Resampling. 68-81 - Xiaoran Zhang, Michelle Noga, Kumaradevan Punithakumar:
Fully Automated Deep Learning Based Segmentation of Normal, Infarcted and Edema Regions from Multiple Cardiac MRI Sequences. 82-91 - Weisheng Li, Linhong Wang, Sheng Qin:
CMS-UNet: Cardiac Multi-task Segmentation in MRI with a U-Shaped Network. 92-101 - Tewodros Weldebirhan Arega, Stéphanie Bricq:
Automatic Myocardial Scar Segmentation from Multi-sequence Cardiac MRI Using Fully Convolutional Densenet with Inception and Squeeze-Excitation Module. 102-117 - Hong Yu, Sen Zha, Yubin Huangfu, Chen Chen, Meng Ding, Jiangyun Li:
Dual Attention U-Net for Multi-sequence Cardiac MR Images Segmentation. 118-127 - Elif Altunok, Ilkay Öksüz:
Accurate Myocardial Pathology Segmentation with Residual U-Net. 128-137 - Zhou Zhao, Nicolas Boutry, Élodie Puybareau:
Stacked and Parallel U-Nets with Multi-output for Myocardial Pathology Segmentation. 138-145 - Feiyan Li, Weisheng Li:
Dual-Path Feature Aggregation Network Combined Multi-layer Fusion for Myocardial Pathology Segmentation with Multi-sequence Cardiac MR. 146-158 - Jun Ma:
Cascaded Framework with Complementary CMR Information for Myocardial Pathology Segmentation. 159-166 - Ke Zhang, Xiahai Zhuang:
Recognition and Standardization of Cardiac MRI Orientation via Multi-tasking Learning and Deep Neural Networks. 167-176
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