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The BICECA component amplifies the local feature sensitivity, whereas the DASPP component expands the network\u2019s information-gathering scope, resulting in elevated segmentation accuracy. Additionally, BINet, a module for joint network loss evaluation, is proposed. It optimizes the segmentation model without affecting the segmentation results. When combined with the DASPP-BICECA module, BINet enhances overall efficiency. The CCTA segmentation algorithm proposed in this study outperformed the other three comparative algorithms, achieving an intersection over Union of 87.37%, Dice of 93.26%, accuracy of 93.12%, mean intersection over Union of 93.68%, mean Dice of 96.63%, and mean pixel accuracy value of 96.55%.<\/jats:p>","DOI":"10.1186\/s42492-024-00157-8","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T06:42:18Z","timestamp":1711089738000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["PlaqueNet: deep learning enabled coronary artery plaque segmentation from coronary computed tomography angiography"],"prefix":"10.1186","volume":"7","author":[{"given":"Linyuan","family":"Wang","sequence":"first","affiliation":[]},{"given":"Xiaofeng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Congyu","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Shu","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yongzhi","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Xiangyun","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Qiong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Weixin","family":"Si","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"key":"157_CR1","doi-asserted-by":"publisher","first-page":"9866114","DOI":"10.1155\/2021\/9866114","volume":"2021","author":"H Wang","year":"2021","unstructured":"Wang H, Wang H, Huang ZL, Su HJ, Gao X, Huang FF (2021) Deep learning-based computed tomography images for quantitative measurement of the correlation between epicardial adipose tissue volume and coronary heart disease. 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