DRRN: Differential rectification & refinement network for ischemic infarct segmentation
Abstract Accurate segmentation of infarct tissue in ischemic stroke is essential to determine the extent of injury and assess the risk and choose optimal treatment for this life‐threatening disease. With the prior knowledge that asymmetric analysis of anatomical structures can provide discriminative...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | CAAI Transactions on Intelligence Technology |
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| Online Access: | https://doi.org/10.1049/cit2.12350 |
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| author | Wenxue Zhou Wenming Yang Qingmin Liao |
| author_facet | Wenxue Zhou Wenming Yang Qingmin Liao |
| author_sort | Wenxue Zhou |
| collection | DOAJ |
| description | Abstract Accurate segmentation of infarct tissue in ischemic stroke is essential to determine the extent of injury and assess the risk and choose optimal treatment for this life‐threatening disease. With the prior knowledge that asymmetric analysis of anatomical structures can provide discriminative information, plenty of symmetry‐based approaches have emerged to detect abnormalities in brain images. However, the inevitable non‐pathological noise has not been fully alleviated and weakened, leading to unsatisfactory results. A novel differential rectification and refinement network (DRRN) for the automatic segmentation of ischemic strokes is proposed. Specifically, a differential feature perception encoder (DFPE) is developed to fully exploit and propagate the bilateral quasi‐symmetry of healthy brains. In DFPE, an erasure‐rectification (ER) module is devised to rectify pseudo‐lesion features caused by non‐pathological noise through utilising discriminant features within the symmetric neighbourhood of the original image. And a differential‐attention (DA) mechanism is also integrated to fully perceive the differences in cross‐axial features and estimate the similarity of long‐range spatial context information. In addition, a crisscross differential feature reinforce module embedded with multiple boundary enhancement attention modules is designed to effectively integrate multi‐scale features and refine textual details and margins of the infarct area. Experimental results on the public ATLAS and Kaggle dataset demonstrate the effectiveness of DRRN over state‐of‐the‐arts. |
| format | Article |
| id | doaj-art-a8a31ae5fc304ba1bab81f0b508d0d14 |
| institution | Kabale University |
| issn | 2468-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj-art-a8a31ae5fc304ba1bab81f0b508d0d142025-01-13T14:05:51ZengWileyCAAI Transactions on Intelligence Technology2468-23222024-12-01961534154710.1049/cit2.12350DRRN: Differential rectification & refinement network for ischemic infarct segmentationWenxue Zhou0Wenming Yang1Qingmin Liao2Shenzhen International Graduate School Tsinghua University Shenzhen ChinaShenzhen International Graduate School Tsinghua University Shenzhen ChinaShenzhen International Graduate School Tsinghua University Shenzhen ChinaAbstract Accurate segmentation of infarct tissue in ischemic stroke is essential to determine the extent of injury and assess the risk and choose optimal treatment for this life‐threatening disease. With the prior knowledge that asymmetric analysis of anatomical structures can provide discriminative information, plenty of symmetry‐based approaches have emerged to detect abnormalities in brain images. However, the inevitable non‐pathological noise has not been fully alleviated and weakened, leading to unsatisfactory results. A novel differential rectification and refinement network (DRRN) for the automatic segmentation of ischemic strokes is proposed. Specifically, a differential feature perception encoder (DFPE) is developed to fully exploit and propagate the bilateral quasi‐symmetry of healthy brains. In DFPE, an erasure‐rectification (ER) module is devised to rectify pseudo‐lesion features caused by non‐pathological noise through utilising discriminant features within the symmetric neighbourhood of the original image. And a differential‐attention (DA) mechanism is also integrated to fully perceive the differences in cross‐axial features and estimate the similarity of long‐range spatial context information. In addition, a crisscross differential feature reinforce module embedded with multiple boundary enhancement attention modules is designed to effectively integrate multi‐scale features and refine textual details and margins of the infarct area. Experimental results on the public ATLAS and Kaggle dataset demonstrate the effectiveness of DRRN over state‐of‐the‐arts.https://doi.org/10.1049/cit2.12350differential attentiondifferential rectificationischemic infarct segmentationnon‐pathological noise reduction |
| spellingShingle | Wenxue Zhou Wenming Yang Qingmin Liao DRRN: Differential rectification & refinement network for ischemic infarct segmentation CAAI Transactions on Intelligence Technology differential attention differential rectification ischemic infarct segmentation non‐pathological noise reduction |
| title | DRRN: Differential rectification & refinement network for ischemic infarct segmentation |
| title_full | DRRN: Differential rectification & refinement network for ischemic infarct segmentation |
| title_fullStr | DRRN: Differential rectification & refinement network for ischemic infarct segmentation |
| title_full_unstemmed | DRRN: Differential rectification & refinement network for ischemic infarct segmentation |
| title_short | DRRN: Differential rectification & refinement network for ischemic infarct segmentation |
| title_sort | drrn differential rectification refinement network for ischemic infarct segmentation |
| topic | differential attention differential rectification ischemic infarct segmentation non‐pathological noise reduction |
| url | https://doi.org/10.1049/cit2.12350 |
| work_keys_str_mv | AT wenxuezhou drrndifferentialrectificationrefinementnetworkforischemicinfarctsegmentation AT wenmingyang drrndifferentialrectificationrefinementnetworkforischemicinfarctsegmentation AT qingminliao drrndifferentialrectificationrefinementnetworkforischemicinfarctsegmentation |