{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T23:41:43Z","timestamp":1744242103784},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684369","type":"print"},{"value":"9781643684376","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"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":[[2023,9,28]]},"abstract":"<jats:p>Due to the expensive segmentation annotation cost, cross-modality medical image segmentation aims to leverage annotations from a source modality (e.g. MRI) to learn a model for target modality (e.g. CT). In this paper, we present a novel method to tackle cross-modality medical image segmentation as semi-supervised multi-modal learning with image translation, which learns better feature representations and is more robust to source annotation scarcity. For semi-supervised multi-modal learning, we develop a deep co-training framework. We address the challenges of co-training on divergent labeled and unlabeled data distributions with a theoretical analysis on multi-view adaptation and propose decomposed multi-view adaptation, which shows better performance than a naive adaptation method on concatenated multi-view features. We further formulate inter-view regularization to alleviate overfitting in deep networks, which regularizes deep co-training networks to be compatible with the underlying data distribution. We perform extensive experiments to evaluate our framework. Our framework significantly outperforms state-of-the-art domain adaptation methods on three segmentation datasets, including two public datasets on cross-modality cardiac substructure segmentation and abdominal multi-organ segmentation and one large scale private dataset on cross-modality brain tissue segmentation. Our code is publicly available at https:\/\/github.com\/zlheui\/DCT.<\/jats:p>","DOI":"10.3233\/faia230633","type":"book-chapter","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:22:27Z","timestamp":1695979347000},"source":"Crossref","is-referenced-by-count":3,"title":["Deep Co-Training for Cross-Modality Medical Image Segmentation"],"prefix":"10.3233","author":[{"given":"Lei","family":"Zhu","sequence":"first","affiliation":[{"name":"Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling Ling","family":"Chan","sequence":"additional","affiliation":[{"name":"Singapore General Hospital, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teck Khim","family":"Ng","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meihui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beng Chin","family":"Ooi","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2023"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230633","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:22:28Z","timestamp":1695979348000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230633"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,28]]},"ISBN":["9781643684369","9781643684376"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230633","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,28]]}}}