{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T17:00:26Z","timestamp":1793034026477,"version":"4.5.0"},"reference-count":42,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2020,8,2]],"date-time":"2020-08-02T00:00:00Z","timestamp":1596326400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2020,9]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>Because the manual contouring process is labor\u2010intensive and time\u2010consuming, segmentation of organs\u2010at\u2010risk (OARs) is a weak link in radiotherapy treatment planning process. Our goal was to develop a synthetic MR (sMR)\u2010aided dual pyramid network (DPN) for rapid and accurate head and neck multi\u2010organ segmentation in order to expedite the treatment planning process.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Forty\u2010five patients\u2019 CT, MR, and manual contours pairs were included as our training dataset. Nineteen OARs were target organs to be segmented. The proposed sMR\u2010aided DPN method featured a deep attention strategy to effectively segment multiple organs. The performance of sMR\u2010aided DPN method was evaluated using five metrics, including Dice similarity coefficient (DSC), Hausdorff distance 95% (HD95), mean surface distance (MSD), residual mean square distance (RMSD), and volume difference. Our method was further validated using the 2015 head and neck challenge data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The contours generated by the proposed method closely resemble the ground truth manual contours, as evidenced by encouraging quantitative results in terms of DSC using the 2015 head and neck challenge data. Mean DSC values of 0.91\u00a0\u00b1\u00a00.02, 0.73\u00a0\u00b1\u00a00.11, 0.96\u00a0\u00b1\u00a00.01, 0.78\u00a0\u00b1\u00a00.09\/0.78\u00a0\u00b1\u00a00.11, 0.88\u00a0\u00b1\u00a00.04\/0.88\u00a0\u00b1\u00a00.06 and 0.86\u00a0\u00b1\u00a00.08\/0.85\u00a0\u00b1\u00a00.1 were achieved for brain stem, chiasm, mandible, left\/right optic nerve, left\/right parotid, and left\/right submandibular, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We demonstrated the feasibility of sMR\u2010aided DPN for head and neck multi\u2010organ delineation on CT images. Our method has shown superiority over the other methods on the 2015 head and neck challenge data results. The proposed method could significantly expedite the treatment planning process by rapidly segmenting multiple OARs.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.14378","type":"journal-article","created":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T11:10:44Z","timestamp":1594379444000},"page":"4294-4302","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Head and neck multi\u2010organ auto\u2010segmentation on CT images aided by synthetic MRI"],"prefix":"10.1002","volume":"47","author":[{"given":"Yingzi","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA 30322 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Lei","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA 30322 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yabo","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA 30322 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tonghe","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA 30322 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA 30322 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojun","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA 30322 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"McDonald","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA 30322 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan J.","family":"Beitler","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA 30322 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Walter J.","family":"Curran","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA 30322 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA 30322 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA 30322 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,8,2]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"key":"e_1_2_7_3_1","unstructured":"RatkoTA DouglasG De SouzaJA BelinsonSE AronsonNRadiotherapy treatments for head and neck cancer update.2014."},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/0360-3016(94)00421-G"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2009.09.062"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.04.002"},{"key":"e_1_2_7_7_1","article-title":"Fully automatic segmentation of head and neck organs using active appearance models","author":"Mannion\u2010Haworth R","year":"2015","journal-title":"MIDAS J"},{"key":"e_1_2_7_8_1","first-page":"434","article-title":"Atlas\u2010Based Auto\u2010segmentation of Head and Neck CT Images","volume":"2008","author":"Han X","year":"2008","journal-title":"Medical Image Computing and Computer\u2010Assisted Intervention \u2013. 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