{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T02:06:27Z","timestamp":1740103587874,"version":"3.37.3"},"reference-count":8,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,8,3]],"date-time":"2020-08-03T00:00:00Z","timestamp":1596412800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2018M631164","XJJ2018254","61876148"],"award-info":[{"award-number":["2018M631164","XJJ2018254","61876148"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2018M631164","XJJ2018254","61876148"],"award-info":[{"award-number":["2018M631164","XJJ2018254","61876148"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2018M631164","XJJ2018254","61876148"],"award-info":[{"award-number":["2018M631164","XJJ2018254","61876148"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2020,8,3]]},"abstract":"<jats:p>Accurate segmentation ofs organs-at-risk (OARs) in computed tomography (CT) is the key to planning treatment in radiation therapy (RT). Manually delineating OARs over hundreds of images of a typical CT scan can be time-consuming and error-prone. Deep convolutional neural networks with specific structures like U-Net have been proven effective for medical image segmentation. In this work, we propose an end-to-end deep neural network for multiorgan segmentation with higher accuracy and lower complexity. Compared with several state-of-the-art methods, the proposed accuracy-complexity adjustment module (ACAM) can increase segmentation accuracy and reduce the model complexity and memory usage simultaneously. An attention-based multiscale aggregation module (MAM) is also proposed for further improvement. Experiment results on chest CT datasets show that the proposed network achieves competitive Dice similarity coefficient results with fewer float-point operations (FLOPs) for multiple organs, which outperforms several state-of-the-art methods.<\/jats:p>","DOI":"10.1155\/2020\/9595687","type":"journal-article","created":{"date-parts":[[2020,8,3]],"date-time":"2020-08-03T23:41:41Z","timestamp":1596498101000},"page":"1-13","source":"Crossref","is-referenced-by-count":1,"title":["A Multiscale-Based Adjustable Convolutional Neural Network for Multiple Organ Segmentation"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3669-3748","authenticated-orcid":true,"given":"Zhiqiang","family":"Tian","sequence":"first","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyi","family":"Song","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohui","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhong","family":"Shi","sequence":"additional","affiliation":[{"name":"Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, 310022, China"},{"name":"Cancer Hospital of the University of Chinese Academy of Sciences, 310022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofu","family":"Yu","sequence":"additional","affiliation":[{"name":"Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, 310022, China"},{"name":"Cancer Hospital of the University of Chinese Academy of Sciences, 310022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.6.1.014001"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"first-page":"3","volume-title":"Unet++: a nested u-net architecture for medical image segmentation","year":"2018","key":"6"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"first-page":"103","volume-title":"Gpipe: efficient training of giant neural networks using pipeline parallelism","year":"2019","key":"27"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1016\/j.mpsur.2014.12.008"},{"volume-title":"Definition of target volume and organs at risk. Biological Target Volume","year":"2006","key":"31"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2015.01.016"}],"container-title":["Wireless Communications and Mobile Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2020\/9595687.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2020\/9595687.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2020\/9595687.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,3]],"date-time":"2020-08-03T23:41:44Z","timestamp":1596498104000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/wcmc\/2020\/9595687\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,3]]},"references-count":8,"alternative-id":["9595687","9595687"],"URL":"https:\/\/doi.org\/10.1155\/2020\/9595687","relation":{},"ISSN":["1530-8669","1530-8677"],"issn-type":[{"type":"print","value":"1530-8669"},{"type":"electronic","value":"1530-8677"}],"subject":[],"published":{"date-parts":[[2020,8,3]]}}}