{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T13:37:45Z","timestamp":1779025065429,"version":"3.51.4"},"reference-count":39,"publisher":"IOP Publishing","issue":"24","license":[{"start":{"date-parts":[[2020,12,9]],"date-time":"2020-12-09T00:00:00Z","timestamp":1607472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2020,12,9]],"date-time":"2020-12-09T00:00:00Z","timestamp":1607472000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"NIH","award":["R01CA188300"],"award-info":[{"award-number":["R01CA188300"]}]},{"name":"National Natural Science Foundation of Shaanxi Province","award":["2019ZDLGY03-02-02"],"award-info":[{"award-number":["2019ZDLGY03-02-02"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2020,12,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Accurate segmentation of organs at risk (OARs) is necessary for adaptive head and neck (H&amp;N) cancer treatment planning, but manual delineation is tedious, slow, and inconsistent. A self-channel-and-spatial-attention neural network (SCSA-Net) is developed for H&amp;N OAR segmentation on CT images. To simultaneously ease the training and improve the segmentation performance, the proposed SCSA-Net utilizes the self-attention ability of the network. Spatial and channel-wise attention learning mechanisms are both employed to adaptively force the network to emphasize the meaningful features and weaken the irrelevant features simultaneously. The proposed network was first evaluated on a public dataset, which includes 48 patients, then on a separate serial CT dataset, which contains ten patients who received weekly diagnostic fan-beam CT scans. On the second dataset, the accuracy of using SCSA-Net to track the parotid and submandibular gland volume changes during radiotherapy treatment was quantified. The Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), average surface distance (ASD), and 95% maximum surface distance (95SD) were calculated on the brainstem, optic chiasm, optic nerves, mandible, parotid glands, and submandibular glands to evaluate the proposed SCSA-Net. The proposed SCSA-Net consistently outperforms the state-of-the-art methods on the public dataset. Specifically, compared with Res-Net and SE-Net, which is constructed from squeeze-and-excitation block equipped residual blocks, the DSC of the optic nerves and submandibular glands is improved by 0.06, 0.03 and 0.05, 0.04 by the SCSA-Net. Moreover, the proposed method achieves statistically significant improvements in terms of DSC on all and eight of nine OARs over Res-Net and SE-Net, respectively. The trained network was able to achieve good segmentation results on the serial dataset, but the results were further improved after fine-tuning of the model using the simulation CT images. For the parotids and submandibular glands, the volume changes of individual patients are highly consistent between the automated and manual segmentation (Pearson\u2019s correlation 0.97\u20130.99). The proposed SCSA-Net is computationally efficient to perform segmentation (sim 2 s\/CT).<\/jats:p>","DOI":"10.1088\/1361-6560\/ab79c3","type":"journal-article","created":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T17:15:25Z","timestamp":1582650925000},"page":"245034","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":41,"title":["Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck CT images"],"prefix":"10.1088","volume":"65","author":[{"given":"Shuiping","family":"Gou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0554-5136","authenticated-orcid":false,"given":"Nuo","family":"Tong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sharon","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuyuan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Chin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Sheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2020,12,9]]},"reference":[{"key":"pmbab79c3bib1","doi-asserted-by":"publisher","first-page":"1378","DOI":"10.1080\/0284186X.2019.1629017","type":"journal-article","article-title":"Inter-observer variability in target delineation increases during adaptive treatment of head-and-neck and lung cancer","volume":"58","author":"Apolle","year":"2019","journal-title":"Acta. Oncol."},{"key":"pmbab79c3bib2","doi-asserted-by":"publisher","first-page":"e703","DOI":"10.1016\/j.ijrobp.2012.02.003","type":"journal-article","article-title":"A system for continual quality improvement of normal tissue delineation for radiation therapy treatment planning","volume":"83","author":"Breunig","year":"2012","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbab79c3bib3","doi-asserted-by":"publisher","first-page":"1284","DOI":"10.1080\/0284186X.2018.1505053","type":"journal-article","article-title":"Adaptive radiotherapy for head and neck cancer","volume":"57","author":"Castelli","year":"2018a","journal-title":"Acta. Oncol."},{"key":"pmbab79c3bib4","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1080\/0284186X.2018.1468086","type":"other","article-title":"Adaptive radiotherapy in head and neck cancer is required to avoid tumor underdose","volume":"57","author":"Castelli","year":"2018b"},{"key":"pmbab79c3bib5","doi-asserted-by":"publisher","first-page":"e266","DOI":"10.1016\/S1470-2045(17)30252-8","type":"journal-article","article-title":"The future of personalised radiotherapy for head and neck cancer","volume":"18","author":"Caudell","year":"2017","journal-title":"Lancet. Oncol."},{"key":"pmbab79c3bib6","doi-asserted-by":"crossref","DOI":"10.54294\/hk5bjs","type":"web-resource","article-title":"A multi-atlas approach for the automatic segmentation of multiple structures in head and neck CT images","author":"Chen","year":"2016"},{"key":"pmbab79c3bib7","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2","type":"book","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","volume":"vol 11211","author":"Chen","year":"2018"},{"key":"pmbab79c3bib8","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1109\/34.927467","type":"journal-article","article-title":"Active appearance models","volume":"23","author":"Cootes","year":"2001","journal-title":"IEEE Pattern Anal. Mach. Intell."},{"key":"pmbab79c3bib9","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1006\/cviu.1995.1004","type":"journal-article","article-title":"Active shape models\u2014their training and application","volume":"61","author":"Cootes","year":"1995","journal-title":"Comput. Vis. Image Underst."},{"key":"pmbab79c3bib10","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1007\/978-3-319-46723-8_19","type":"book","article-title":"Deep neural networks for fast segmentation of 3D medical images","author":"Fritscher","year":"2016"},{"key":"pmbab79c3bib11","doi-asserted-by":"publisher","first-page":"1910","DOI":"10.1118\/1.4871623","type":"journal-article","article-title":"Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours","volume":"41","author":"Fritscher","year":"2014","journal-title":"Med. Phys."},{"key":"pmbab79c3bib12","first-page":"249","type":"journal-article","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume":"9","author":"Glorot","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"pmbab79c3bib13","doi-asserted-by":"publisher","first-page":"8685","DOI":"10.1073\/pnas.0701361104","type":"journal-article","article-title":"The human disease network","volume":"104","author":"Goh","year":"2007","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"pmbab79c3bib14","doi-asserted-by":"publisher","first-page":"e292","DOI":"10.1016\/S1470-2045(12)70237-1","type":"journal-article","article-title":"Radiotherapy for head and neck tumours in 2012 and beyond: conformal, tailored, and adaptive?","volume":"13","author":"Gr\u00e9goire","year":"2012","journal-title":"Lancet Oncol."},{"key":"pmbab79c3bib15","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1007\/978-3-540-85990-1_52","type":"book","article-title":"Atlas-based auto-segmentation of head and neck CT images","author":"Han","year":"2008"},{"key":"pmbab79c3bib16","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.media.2018.08.005","type":"journal-article","article-title":"Spine-GAN: semantic segmentation of multiple spinal structures","volume":"50","author":"Han","year":"2018","journal-title":"Med. Image Anal."},{"key":"pmbab79c3bib17","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","type":"journal-article","article-title":"Deep residual learning for image recognition","author":"He","year":"2016","journal-title":"2016 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)"},{"key":"pmbab79c3bib18","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.semradonc.2019.02.008","type":"journal-article","article-title":"Head and neck cancer adaptive radiation therapy (ART): conceptual considerations for the informed clinician","volume":"29","author":"Heukelom","year":"2019","journal-title":"Semin. Radiat. Oncol."},{"key":"pmbab79c3bib19","doi-asserted-by":"publisher","first-page":"7132","DOI":"10.1109\/CVPR.2018.00745","type":"journal-article","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018","journal-title":"2018 IEEE\/CVF Conf. on Computer Vision and Pattern Recognition"},{"key":"pmbab79c3bib20","article-title":"Adam: a method for stochastic optimization","author":"Kingma","year":"2015","type":"preprint"},{"key":"pmbab79c3bib21","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2017.549","type":"preprint","article-title":"RefineNet: multi-path refinement networks for high-resolution semantic segmentation","author":"Lin","year":"2017"},{"key":"pmbab79c3bib22","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","type":"journal-article","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Long","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"pmbab79c3bib23","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1186\/1748-717X-7-160","type":"journal-article","article-title":"Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer","volume":"7","author":"Macchia","year":"2012","journal-title":"Radiat. Oncol."},{"key":"pmbab79c3bib24","doi-asserted-by":"crossref","DOI":"10.54294\/e86siq","type":"web-resource","article-title":"Fully automatic segmentation of head and neck organs using active appearance models","author":"Mannion-Haworth","year":"2016"},{"key":"pmbab79c3bib25","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1148\/radiol.14132871","type":"journal-article","article-title":"Quality assurance assessment of diagnostic and radiation therapy-simulation CT image registration for head and neck radiation therapy: anatomic region of interest-based comparison of rigid and deformable algorithms","volume":"274","author":"Mohamed","year":"2015","journal-title":"Radiology"},{"key":"pmbab79c3bib26","doi-asserted-by":"publisher","first-page":"807","DOI":"10.5555\/3104322.3104425","type":"journal-article","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"Nair","year":"2010","journal-title":"ICML\u201910: Proc. of the 27th Int. Conf. on Int. Conf. on Machine Learning"},{"key":"pmbab79c3bib27","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1007\/978-3-030-00937-3_43","type":"book","article-title":"ASDNet: attention based semi-supervised deep networks for medical image segmentation","author":"Nie","year":"2018"},{"key":"pmbab79c3bib28","doi-asserted-by":"publisher","first-page":"e427","DOI":"10.1016\/j.ijrobp.2012.04.003","type":"journal-article","article-title":"Automatic segmentation and online virtualCT in head-and-neck adaptive radiation therapy","volume":"84","author":"Peroni","year":"2012","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbab79c3bib29","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/j.ijrobp.2015.01.033","type":"journal-article","article-title":"Near real-time assessment of anatomic and dosimetric variations for head and neck radiation therapy via graphics processing unit-based dose deformation framework","volume":"92","author":"Qi","year":"2015","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbab79c3bib30","doi-asserted-by":"publisher","first-page":"2020","DOI":"10.1002\/mp.12197","type":"journal-article","article-title":"Evaluation of segmentation methods on head and neck CT: auto-segmentation challenge 2015","volume":"44","author":"Raudaschl","year":"2017","journal-title":"Med. Phys."},{"key":"pmbab79c3bib31","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/726268","type":"journal-article","article-title":"Evaluation of deformable image registration methods for dose monitoring in head and neck radiotherapy","volume":"2015","author":"Rigaud","year":"2015","journal-title":"Biomed Res. Int."},{"key":"pmbab79c3bib32","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1007\/978-3-030-00928-1_48","type":"book","article-title":"Concurrent spatial and channel \u2018squeeze & excitation\u2019 in fully convolutional networks","author":"Roy","year":"2018"},{"key":"pmbab79c3bib33","doi-asserted-by":"publisher","first-page":"2669","DOI":"10.1002\/mp.13553","type":"journal-article","article-title":"Shape constrained fully convolutional denseNet with adversarial training for multiorgan segmentation on head and neck CT and low-field MR images","volume":"46","author":"Tong","year":"2019","journal-title":"Med. Phys."},{"key":"pmbab79c3bib34","doi-asserted-by":"publisher","first-page":"4558","DOI":"10.1002\/mp.13147","type":"journal-article","article-title":"Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks","volume":"45","author":"Tong","year":"2018","journal-title":"Med. Phys."},{"key":"pmbab79c3bib35","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1109\/TIP.2017.2768621","type":"journal-article","article-title":"Hierarchical vertex regression-based segmentation of head and neck ct images for radiotherapy planning","volume":"27","author":"Wang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"pmbab79c3bib36","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1007\/978-3-030-00931-1_70","type":"book","article-title":"3D segmentation with exponential logarithmic loss for highly unbalanced object sizes","author":"Wong","year":"2018"},{"key":"pmbab79c3bib37","doi-asserted-by":"publisher","first-page":"2092","DOI":"10.1109\/TMI.2019.2893944","type":"journal-article","article-title":"Attention residual learning for skin lesion classification","volume":"38","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbab79c3bib38","doi-asserted-by":"publisher","first-page":"2149","DOI":"10.1109\/TMI.2018.2821244","type":"journal-article","article-title":"Automatic segmentation of acute ischemic stroke from DWI using 3-D fully convolutional DenseNets","volume":"37","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbab79c3bib39","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1002\/mp.13300","type":"journal-article","article-title":"AnatomyNet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy","volume":"46","author":"Zhu","year":"2019","journal-title":"Med. Phys."}],"container-title":["Physics in Medicine &amp; Biology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab79c3","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab79c3\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab79c3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab79c3\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab79c3\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab79c3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab79c3\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab79c3\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab79c3\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab79c3\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab79c3\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab79c3","content-type":"text\/html","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T14:47:07Z","timestamp":1765637227000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ab79c3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,9]]},"references-count":39,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2020,12,9]]},"published-print":{"date-parts":[[2020,12,21]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/ab79c3","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,9]]},"assertion":[{"value":"Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck CT images","name":"article_title","label":"Article Title"},{"value":"Physics in Medicine & Biology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2020 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2019-09-05","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2020-02-25","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2020-12-09","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}