{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:37:10Z","timestamp":1761176230398,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"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":[[2025,10,21]]},"abstract":"<jats:p>Accurate automated measurement of diaphragmatic thickness in ultrasound imaging is a critical challenging task for respiratory function assessment, primarily due to difficulties in precise fascial identification. And ultrasound visualization of the diaphragm is characterized by unique challenges, including discontinuous and blurred boundary delineations caused by imaging artifacts, as well as interference and influence from adjacent muscular reverberations. These problems are further compounded by subjects\u2019 pose variations during image acquisition. To address these challenges, we introduce IBS-Net, an innovative triple-branch interactive segmentation network that synergistically combines boundary regression with auxiliary task learning to optimize feature representation in segmentation task. Moreover, Our framework incorporates two innovative module: an Adaptive Fusion Module (AFM) that enables multi-scale hierarchical feature refinement for precise boundary characterization, and a Cross Interactive Module (CIM) that employs parallel-encoded feature extraction to simultaneously achieve accurate fascial localization while preserving structural topology. These complementary mechanisms effectively resolve spatial feature inconsistencies, facilitating robust multi-level feature integration. Comprehensive experimental results demonstrate that IBS-Net achieves statistically significant improvements of 8.9% in Dice similarity coefficient and 8.05% in Jaccard index compared to conventional methods. Moreover, to verify the effectiveness of the proposed method, we extended it to other publicly available BUSI dataset for experimentation. The results demonstrate that our method is competitive in terms of both accuracy and completeness in the identification of fuzzy boundaries in ultrasound images.<\/jats:p>","DOI":"10.3233\/faia251172","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:53:38Z","timestamp":1761126818000},"source":"Crossref","is-referenced-by-count":0,"title":["IBS-Net: Advancing Implicit Boundary-Aware Segmentation for Diaphragm Ultrasound Analysis"],"prefix":"10.3233","author":[{"given":"Baike","family":"Shi","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}]},{"given":"Yikang","family":"He","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, Zhongda Hospital, Southeast University, China"}]},{"given":"Chenlong","family":"Miao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}]},{"given":"Wenbo","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}]},{"given":"Tianyi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}]},{"given":"Hui","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}]},{"given":"Jianmin","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Technology, Xizang Minzu University, Xianyang, China"}]},{"given":"Rongjun","family":"Ge","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, Nanjing, China"}]},{"given":"Guangquan","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Biological Science and Medical Engineering, Southeast University, Nanjing, China"}]},{"given":"Yang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251172","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:53:38Z","timestamp":1761126818000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251172"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251172","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}