{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T14:40:43Z","timestamp":1787496043308,"version":"build-2736575974"},"reference-count":33,"publisher":"IOP Publishing","issue":"1","license":[{"start":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T00:00:00Z","timestamp":1703808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/page\/copyright"},{"start":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T00:00:00Z","timestamp":1703808000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100005145","name":"Basic Research Program of Jiangsu Province","doi-asserted-by":"crossref","award":["BK20210173"],"award-info":[{"award-number":["BK20210173"]}],"id":[{"id":"10.13039\/501100005145","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["12090023"],"award-info":[{"award-number":["12090023"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Ministry of Science and Technology of China","award":["2020YFA0713803"],"award-info":[{"award-number":["2020YFA0713803"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Phys. Eng. Express"],"published-print":{"date-parts":[[2024,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Rib segmentation in 2D chest x-ray images is a crucial and challenging task. On one hand, chest x-ray images serve as the most prevalent form of medical imaging due to their convenience, affordability, and minimal radiation exposure. However, on the other hand, these images present intricate challenges including overlapping anatomical structures, substantial noise and artifacts, inherent anatomical complexity. Currently, most methods employ deep convolutional networks for rib segmentation, necessitating an extensive quantity of accurately labeled data for effective training. Nonetheless, achieving precise pixel-level labeling in chest x-ray images presents a notable difficulty. Additionally, many methods neglect the challenge of predicting fractured results and subsequent post-processing difficulties. In contrast, CT images benefit from being able to directly label as the 3D structure and patterns of organs or tissues. In this paper, we redesign rib segmentation task for chest x-ray images and propose a concise and efficient cross-modal method based on unsupervised domain adaptation with centerline loss function to prevent result discontinuity and address rigorous post-processing. We utilize digital reconstruction radiography images and the labels generated from 3D CT images to guide rib segmentation on unlabeled 2D chest x-ray images. Remarkably, our model achieved a higher dice score on the test samples and the results are highly interpretable, without requiring any annotated rib markings on chest x-ray images. Our code and demo will be released in \u2018\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/jialin-zhao\/RibsegBasedonUDA\" xlink:type=\"simple\">https:\/\/github.com\/jialin-zhao\/RibsegBasedonUDA<\/jats:ext-link>\n                    \u2019.\n                  <\/jats:p>","DOI":"10.1088\/2057-1976\/ad1663","type":"journal-article","created":{"date-parts":[[2023,12,17]],"date-time":"2023-12-17T17:20:48Z","timestamp":1702833648000},"page":"015021","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Rib segmentation in chest x-ray images based on unsupervised domain adaptation"],"prefix":"10.1088","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5330-3232","authenticated-orcid":true,"given":"Jialin","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4499-7207","authenticated-orcid":true,"given":"Ziwei","family":"Nie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoping","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2023,12,29]]},"reference":[{"key":"bpexad1663bib1","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1109\/TMI.2003.816958","article-title":"Noise reduction for magnetic resonance images via adaptive multiscale products thresholding","volume":"22","author":"Bao","year":"2003","journal-title":"IEEE Trans. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2023-10-10","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-12-17","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-12-29","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}