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Conference Paper: Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non–Contrast CT Images
Title | Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non–Contrast CT Images |
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Authors | |
Keywords | Computer aided diagnosis Infarct segmentation Acute ischemic stroke Attention mechanism Deep learning |
Issue Date | 2021 |
Publisher | Springer. |
Citation | International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Strasbourg, France, September 27–October 1, 2021. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021: 24th International Conference, Strasbourg, France, September 27 – October 1, 2021, Proceedings, Part VII, p. 432-441 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/316290 |
DC Field | Value | Language |
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dc.contributor.author | Liang, K | - |
dc.contributor.author | Han, K | - |
dc.contributor.author | Li, X | - |
dc.contributor.author | Cheng, X | - |
dc.contributor.author | Li, Y | - |
dc.contributor.author | Wang, Y | - |
dc.contributor.author | Yu, Y | - |
dc.date.accessioned | 2022-09-02T06:08:53Z | - |
dc.date.available | 2022-09-02T06:08:53Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Strasbourg, France, September 27–October 1, 2021. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021: 24th International Conference, Strasbourg, France, September 27 – October 1, 2021, Proceedings, Part VII, p. 432-441 | - |
dc.identifier.uri | http://hdl.handle.net/10722/316290 | - |
dc.description.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. | - |
dc.language | eng | - |
dc.publisher | Springer. | - |
dc.relation.ispartof | Medical Image Computing and Computer Assisted Intervention – MICCAI 2021: 24th International Conference, Strasbourg, France, September 27 – October 1, 2021, Proceedings, Part VII | - |
dc.subject | Computer aided diagnosis | - |
dc.subject | Infarct segmentation | - |
dc.subject | Acute ischemic stroke | - |
dc.subject | Attention mechanism | - |
dc.subject | Deep learning | - |
dc.title | Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non–Contrast CT Images | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Yu, Y: yzyu@cs.hku.hk | - |
dc.identifier.authority | Yu, Y=rp01415 | - |
dc.identifier.hkuros | 336357 | - |
dc.identifier.spage | 432 | - |
dc.identifier.epage | 441 | - |
dc.publisher.place | Switzerland | - |