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Opt. Express"],"published-print":{"date-parts":[[2025,5,1]]},"abstract":"<jats:p>Binary semantic segmentation in computer vision is a fundamental problem. As a model-based segmentation method, the graph-cut approach was one of the most successful binary segmentation methods thanks to its global optimality guarantee of the solutions and its practical polynomial-time complexity. Recently, many deep learning (DL) based methods have been developed for this task and yielded remarkable performance, resulting in a paradigm shift in this field. To combine the strengths of both approaches, we propose in this study to integrate the graph-cut approach into a deep learning network for end-to-end learning. Unfortunately, backward propagation through the graph-cut module in the DL network is challenging due to the combinatorial nature of the graph-cut algorithm. To tackle this challenge, we propose a novel residual graph-cut loss and a quasi-residual connection, enabling the backward propagation of the gradients of the residual graph-cut loss for effective feature learning guided by the graph-cut segmentation model. In the inference phase, globally optimal segmentation is achieved with respect to the graph-cut energy defined on the optimized image features learned from DL networks. Experiments on the public AZH chronic wound data set and the pancreas cancer data set from the medical segmentation decathlon (MSD) demonstrated promising segmentation accuracy and improved robustness against adversarial attacks.<\/jats:p>","DOI":"10.1364\/boe.555206","type":"journal-article","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T11:00:21Z","timestamp":1745319621000},"page":"1999","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["<i>gc<\/i>\n                    DLSeg: integrating graph-cut into deep learning for binary semantic segmentation"],"prefix":"10.1364","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5717-5034","authenticated-orcid":true,"given":"Hui","family":"Xie","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/036jqmy94","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiyu","family":"Xu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/036jqmy94","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya Xing","family":"Wang","sequence":"additional","affiliation":[{"name":"Capital University of Medical Science"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Buatti","sequence":"additional","affiliation":[{"name":"The University of Iowa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Wu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/036jqmy94","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2025,4,23]]},"reference":[{"key":"boe-16-5-1999-R1","doi-asserted-by":"publisher","first-page":"3523","DOI":"10.1109\/TPAMI.2021.3059968","volume":"44","author":"Minaee","year":"2021","journal-title":"IEEE Trans. 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