Abstract is missing.
- Stacked BCDU-Net with Semantic CMR Synthesis: Application to Myocardial Pathology Segmentation ChallengeCarlos Martín-Isla, Maryam Asadi-Aghbolaghi, Polyxeni Gkontra, Víctor M. Campello, Sergio Escalera, Karim Lekadir. 1-16 [doi]
- EfficientSeg: A Simple But Efficient Solution to Myocardial Pathology Segmentation ChallengeJianpeng Zhang, Yutong Xie, Zhibin Liao, Johan Verjans, Yong Xia. 17-25 [doi]
- Two-Stage Method for Segmentation of the Myocardial Scars and Edema on Multi-sequence Cardiac Magnetic ResonanceYanfei Liu, Maodan Zhang, Qi Zhan, Dongdong Gu, Guocai Liu. 26-36 [doi]
- Multi-modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance ImagesZhen Zhang, Chenyu Liu, Wangbin Ding, Sihan Wang, Chenhao Pei, Mingjing Yang, Liqin Huang. 37-48 [doi]
- Myocardial Edema and Scar Segmentation Using a Coarse-to-Fine Framework with Weighted EnsembleShuwei Zhai, Ran Gu, Wenhui Lei, Guotai Wang. 49-59 [doi]
- Exploring Ensemble Applications for Multi-sequence Myocardial Pathology SegmentationMarkus J. Ankenbrand, David Lohr, Laura Maria Schreiber. 60-67 [doi]
- Max-Fusion U-Net for Multi-modal Pathology Segmentation with Attention and Dynamic ResamplingHaochuan Jiang, Chengjia Wang, Agisilaos Chartsias, Sotirios A. Tsaftaris. 68-81 [doi]
- Fully Automated Deep Learning Based Segmentation of Normal, Infarcted and Edema Regions from Multiple Cardiac MRI SequencesXiaoran Zhang, Michelle Noga, Kumaradevan Punithakumar. 82-91 [doi]
- CMS-UNet: Cardiac Multi-task Segmentation in MRI with a U-Shaped NetworkWeisheng Li 0001, Linhong Wang, Sheng Qin. 92-101 [doi]
- Automatic Myocardial Scar Segmentation from Multi-sequence Cardiac MRI Using Fully Convolutional Densenet with Inception and Squeeze-Excitation ModuleTewodros Weldebirhan Arega, Stéphanie Bricq. 102-117 [doi]
- Dual Attention U-Net for Multi-sequence Cardiac MR Images SegmentationHong Yu, Sen Zha, Yubin Huangfu, Chen Chen 0001, Meng Ding, Jiangyun Li. 118-127 [doi]
- Accurate Myocardial Pathology Segmentation with Residual U-NetElif Altunok, Ilkay Öksüz. 128-137 [doi]
- Stacked and Parallel U-Nets with Multi-output for Myocardial Pathology SegmentationZhou Zhao, Nicolas Boutry, Élodie Puybareau. 138-145 [doi]
- Dual-Path Feature Aggregation Network Combined Multi-layer Fusion for Myocardial Pathology Segmentation with Multi-sequence Cardiac MRFeiyan Li, Weisheng Li 0001. 146-158 [doi]
- Cascaded Framework with Complementary CMR Information for Myocardial Pathology SegmentationJun Ma. 159-166 [doi]
- Recognition and Standardization of Cardiac MRI Orientation via Multi-tasking Learning and Deep Neural NetworksKe Zhang, Xiahai Zhuang. 167-176 [doi]