{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T01:19:54Z","timestamp":1782177594448,"version":"3.54.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684444","type":"print"},{"value":"9781643684451","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>One of the crucial duties in clinical surgery is medical image segmentation. Deep learning and fully convolutional neural networks are currently being employed for image segmentation and have achieved cutting-edge outcomes on numerous publicly available benchmark datasets. Shape is more significant than visual texture, and typical CNNs are not robust and interpretable. As a result, we suggest the Shape Attention Net (DFSNet), a new architecture that emphasizes robustness and interpretability. This architecture employs a dual-stream method to simultaneously record rich shape-related information and conventional texture streams. A dual attention decoder module is used to learn multi-resolution saliency maps, and a direction field distance prediction pipeline is utilized for fixing segmentation detail. On two sizable public datasets for heart MRI image segmentation, SUN09 and ACDC, our technique produced cutting-edge results.<\/jats:p>","DOI":"10.3233\/faia230870","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:55:31Z","timestamp":1701446131000},"source":"Crossref","is-referenced-by-count":1,"title":["Directional Feature Maps Learning for Interpretable Cardiac Image Segmentation"],"prefix":"10.3233","author":[{"given":"Tiantian","family":"Yang","sequence":"first","affiliation":[{"name":"Wuhan Textile University, WuHan 430200, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Li","sequence":"additional","affiliation":[{"name":"Wuhan Textile University, WuHan 430200, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Advances in Artificial Intelligence, Big Data and Algorithms"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230870","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:55:32Z","timestamp":1701446132000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230870"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9781643684444","9781643684451"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230870","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]}}}