{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:37Z","timestamp":1761176137199,"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>Three-dimensional medical image segmentation plays a significant role in clinical diagnosis, treatment planning, and disease research, as it provides doctors with precise anatomical and lesion information and improves the accuracy and efficiency of medical decision-making. However, most existing 3D segmentation approaches rely heavily on densely volumetric data and often fail to perform segmentation properly for incomplete 3D volume acquisition, i.e., missing slices. In this work, we present InterFrameNet, a framework designed to predict intermediate lesion structures by modeling spatial relationships across frames, enabling robust segmentation performance under sparse acquisition conditions, without requiring full-volume information. Our method explicitly models cross-frame spatial continuity and leverages structural relationships between available frames to accurately infer missing lesion regions. This design significantly reduces the dependence on consecutive frames while fully exploiting contextual anatomical information. Extensive experiments on brain lesion datasets demonstrate that our approach achieves robust segmentation performance under sparse acquisition settings, offering a practical solution to maximize usability of incomplete clinical imaging data.<\/jats:p>","DOI":"10.3233\/faia250871","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:44:40Z","timestamp":1761126280000},"source":"Crossref","is-referenced-by-count":0,"title":["Beyond Slice-by-Slice: 3D Lesion Segmentation via Cross-Frame Prediction"],"prefix":"10.3233","author":[{"given":"Hongpeng","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of South Carolina"}]},{"given":"Yingxin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University"}]},{"given":"Xiangyu","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of South Carolina"}]},{"given":"Srihari","family":"Nelakuditi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of South Carolina"}]},{"given":"Yan","family":"Tong","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of South Carolina"}]},{"given":"Shiqiang","family":"Ma","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences"}]},{"given":"Fei","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250871","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:44:41Z","timestamp":1761126281000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250871"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250871","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]]}}}