| Title | Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-contrast CT Images |
| Authors | Liang, Kongming Han, Kai Li, Xiuli Cheng, Xiaoqing Li, Yiming Wang, Yizhou Yu, Yizhou |
| Affiliation | Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Pattern Recognit & Intelligent Syst Lab, Beijing, Peoples R China Deepwise AI Lab, Beijing, Peoples R China Nanjing Univ, Sch Med, Jinling Hosp, Dept Med Imaging, Nanjing, Jiangsu, Peoples R China Peking Univ, Dept Comp Sci & Technol, Beijing, Peoples R China Univ Hong Kong, Pokfulam, Hong Kong, Peoples R China |
| Keywords | STROKE |
| Issue Date | 2021 |
| Publisher | MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT VII |
| Abstract | Quantitative estimation of the acute ischemic infarct is crucial to improve neurological outcomes of the patients with stroke symptoms. Since the density of lesions is subtle and can be confounded by normal physiologic changes, anatomical asymmetry provides useful information to differentiate the ischemic and healthy brain tissue. In this paper, we propose a symmetry enhanced attention network (SEAN) for acute ischemic infarct segmentation. Our proposed network automatically transforms an input CT image into the standard space where the brain tissue is bilaterally symmetric. The transformed image is further processed by a U-shape network integrated with the proposed symmetry enhanced attention for pixel-wise labelling. The symmetry enhanced attention can efficiently capture context information from the opposite side of the image by estimating long-range dependencies. Experimental results show that the proposed SEAN outperforms some symmetry-based state-of-the-art methods in terms of both dice coefficient and infarct localization. |
| URI | http://hdl.handle.net/20.500.11897/628896 |
| ISBN | 978-3-030-87234-2; 978-3-030-87233-5 |
| ISSN | 0302-9743 |
| DOI | 10.1007/978-3-030-87234-2_41 |
| Indexed | CPCI-S(ISTP) |
| Appears in Collections: | 信息科学技术学院 |
