{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T14:30:58Z","timestamp":1787495458218,"version":"build-2736575974"},"reference-count":15,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,7,17]],"date-time":"2020-07-17T00:00:00Z","timestamp":1594944000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Xinjiang Uygur Autonomous Region\u2019s Major Science and Technology Project in 2017","award":["2017A03006-1"],"award-info":[{"award-number":["2017A03006-1"]}]},{"name":"Xinjiang Uygur Autonomous Region\u2019s Major Science and Technology Project in 2017","award":["201805060ZD11CG44"],"award-info":[{"award-number":["201805060ZD11CG44"]}]},{"DOI":"10.13039\/501100008387","name":"Xi\u2019an Science and Technology Bureau","doi-asserted-by":"crossref","award":["2017A03006-1"],"award-info":[{"award-number":["2017A03006-1"]}],"id":[{"id":"10.13039\/501100008387","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100008387","name":"Xi\u2019an Science and Technology Bureau","doi-asserted-by":"crossref","award":["201805060ZD11CG44"],"award-info":[{"award-number":["201805060ZD11CG44"]}],"id":[{"id":"10.13039\/501100008387","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Healthcare Engineering"],"published-print":{"date-parts":[[2020,7,17]]},"abstract":"<jats:p>Automatic bone segmentation from a chest radiograph is an important and challenging task in medical image analysis. However, a chest radiograph contains numerous artifacts and tissue shadows, such as trachea, blood vessels, and lung veins, which limit the accuracy of traditional segmentation methods, such as thresholding and contour-related techniques. Deep learning has recently achieved excellent segmentation of some organs, such as the pancreas and the hippocampus. However, the insufficiency of annotated datasets impedes clavicle and rib segmentation from chest X-rays. We have constructed a dataset of chest X-rays with a raw chest radiograph and four annotated images showing the clavicles, anterior ribs, posterior ribs, and all bones (the complete set of ribs and clavicle). On the basis of a sufficient dataset, a multitask dense connection U-Net (MDU-Net) is proposed to address the challenge of bone segmentation from a chest radiograph. We first combine the U-Net multiscale feature fusion method, DenseNet dense connection, and multitasking mechanism to construct the proposed network referred to as MDU-Net. We then present a mask encoding mechanism that can force the network to learn the background features. Transfer learning is ultimately introduced to help the network extract sufficient features. We evaluate the proposed network by fourfold cross validation on 88 chest radiography images. The proposed method achieves the average DSC (Dice similarity coefficient) values of 93.78%, 80.95%, 89.06%, and 88.38% in clavicle segmentation, anterior rib segmentation, posterior rib segmentation, and segmentation of all bones, respectively.<\/jats:p>","DOI":"10.1155\/2020\/2785464","type":"journal-article","created":{"date-parts":[[2020,7,17]],"date-time":"2020-07-17T19:31:07Z","timestamp":1595014267000},"page":"1-9","source":"Crossref","is-referenced-by-count":26,"title":["MDU-Net: A Convolutional Network for Clavicle and Rib Segmentation from a Chest Radiograph"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3142-4140","authenticated-orcid":true,"given":"Wenjing","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3168-0689","authenticated-orcid":true,"given":"Hongwei","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8127-9151","authenticated-orcid":true,"given":"Qirong","family":"Bu","sequence":"additional","affiliation":[{"name":"Department of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Information Science and Technology, Northwest University, Xi\u2019an 710127, 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of Advanced Networking and Intelligent Information Services, School of Information Science and Technology, Northwest University, Xi'an 710127, Shaanxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaohui","family":"Zhu","sequence":"additional","affiliation":[{"name":"Chest Hospital of Xinjiang Uyghur Autonomous Region of the PRC, Xinjiang Uygur Autonomous Region, Urumqi 830049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3008-1783","authenticated-orcid":true,"given":"Zhongyuanlong","family":"Chen","sequence":"additional","affiliation":[{"name":"Chest Hospital of Xinjiang Uyghur Autonomous Region of the PRC, Xinjiang Uygur Autonomous Region, Urumqi 830049, 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