Name
Abstract | ||
|---|---|---|
For patients with dilated cardiomyopathy (DCM), fast and accurate diagnosis is important to save lives. MRI is a non-invasive, effective medical imaging method that allows doctors to diagnose DCM. However, manual and semi-automatic segmentation is subjective, non-reproducible and time-consuming task. In this paper, a new attention-based convolutional encoder-decoder network is proposed to automatically segment my-ocardium in DCM, which assisting the doctor to quickly diagnose. In the proposed method, the attention mechanism module is used, which is able to fully highlight useful features that facilitate segmentation while suppress useless features that are not conducive to segmentation. Combining with the multi-scale convolution, our encoder-decoder network can accurately segment the my-ocardium in DCM. We verified our approach on 1155 myocardial MRI. Our network achieves the most advanced segmentation performance on the cardiac DCM dataset. Experiment results demonstrate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/COMPSAC.2019.10245 | 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC) |
Keywords | DocType | Volume |
attention, DCM segmentation, encoder-decoder, MRI | Conference | 2 |
ISSN | ISBN | Citations |
0730-3157 | 978-1-7281-2607-4 | 0 |
PageRank | References | Authors |
0.34 | 5 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Luo | 1 | 58 | 17.22 |
| Canghong Shi | 2 | 1 | 1.71 |
| Xian Zhang | 3 | 0 | 1.35 |
| Jing Peng | 4 | 61 | 18.34 |
| Xiaojie Li | 5 | 19 | 4.38 |
| Yucheng Chen | 6 | 0 | 1.69 |