{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T15:23:24Z","timestamp":1770564204721,"version":"3.49.0"},"reference-count":26,"publisher":"SPIE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,3,13]]},"DOI":"10.1117\/12.2513229","type":"proceedings-article","created":{"date-parts":[[2019,3,13]],"date-time":"2019-03-13T23:02:27Z","timestamp":1552518147000},"page":"8","source":"Crossref","is-referenced-by-count":4,"title":["Automatic multi-modality segmentation of gross tumor volume for head and neck cancer radiotherapy using 3D U-Net"],"prefix":"10.1117","author":[{"given":"Zhe","family":"Guo","sequence":"first","affiliation":[]},{"given":"Ning","family":"Guo","sequence":"first","affiliation":[]},{"given":"Quanzheng","family":"Li","sequence":"first","affiliation":[]},{"given":"Kuang","family":"Gong","sequence":"first","affiliation":[]}],"member":"189","reference":[{"issue":"1","key":"c1","first-page":"7","article-title":"Cancer statistics, 2016","volume":"66","author":"Siegel","year":"2016","journal-title":"CA: a cancer journal for clinicians"},{"key":"c2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2006.01.014"},{"key":"c3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2009.09.062"},{"issue":"8","key":"c4","first-page":"1369","article-title":"A combined PET\/CT scanner for clinical oncology","volume":"41","author":"Beyer","year":"2000","journal-title":"Journal of nuclear medicine"},{"key":"c5","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2311030271"},{"key":"c6","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2013.05.004"},{"key":"c7","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1109\/ISBI.2018.8363717","article-title":"Medical image segmentation based on multi-modal convolutional neural network: Study on image fusion schemes","volume-title":"Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium","author":"Guo","year":"2018"},{"key":"c8","doi-asserted-by":"publisher","DOI":"10.1118\/1.4948679"},{"key":"c9","doi-asserted-by":"publisher","DOI":"10.1118\/1.4871623"},{"key":"c10","doi-asserted-by":"publisher","DOI":"10.1118\/1.4927567"},{"key":"c11","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/8612519"},{"key":"c12","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/61\/17\/6553"},{"key":"c13","doi-asserted-by":"publisher","DOI":"10.1118\/1.3654160"},{"key":"c14","doi-asserted-by":"publisher","DOI":"10.1134\/S1054661818010054"},{"key":"c15","article-title":"Tumor to cervical spinal cord standardized uptake ratio (SUR) improves the reproducibility of 18F-FDG-PET based tumor segmentation in head and neck squamous cell carcinoma in a multicenter setting","volume-title":"Radiotherapy and Oncology","author":"van den Bosch","year":"2018"},{"key":"c16","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12045"},{"key":"c17","doi-asserted-by":"publisher","DOI":"10.1002\/mp.2018.45.issue-5"},{"key":"c18","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2536809"},{"key":"c19","article-title":"Iterative PET image reconstruction using convolutional neural network representation","volume-title":"IEEE transactions on medical imaging","author":"Gong","year":"2018"},{"key":"c20","first-page":"10","article-title":"A deep metric for multimodal registration","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Simonovsky","year":"2016"},{"key":"c21","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1109\/3DV.2016.79","article-title":"V-net: Fully convolutional neural networks for volumetric medical image segmentation","volume-title":"3D Vision (3DV), 2016 Fourth International Conference","author":"Milletari","year":"2016"},{"key":"c22","first-page":"424","article-title":"3D U-Net: learning dense volumetric segmentation from sparse annotation","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"\u00c7i\u00e7ek","year":"2016"},{"key":"c23","article-title":"Data from Head-Neck-PET-CT","volume-title":"The Cancer Imaging Archive","author":"Martin","year":"2017"},{"key":"c24","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-013-9622-7"},{"key":"c25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67558-9"},{"key":"c26","first-page":"265","article-title":"Tensorflow: a system for large-scale machine learning","volume-title":"OSDI","author":"Abadi","year":"2016"}],"event":{"name":"Computer-Aided Diagnosis","location":"San Diego, United States","start":{"date-parts":[[2019,2,16]]},"end":{"date-parts":[[2019,2,21]]}},"container-title":["Medical Imaging 2019: Computer-Aided Diagnosis"],"original-title":[],"deposited":{"date-parts":[[2019,4,1]],"date-time":"2019-04-01T16:45:10Z","timestamp":1554137110000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/10950\/2513229\/Automatic-multi-modality-segmentation-of-gross-tumor-volume-for-head\/10.1117\/12.2513229.full"}},"subtitle":[],"editor":[{"given":"Horst K.","family":"Hahn","sequence":"first","affiliation":[]},{"given":"Kensaku","family":"Mori","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2019,3,13]]},"references-count":26,"URL":"https:\/\/doi.org\/10.1117\/12.2513229","relation":{},"subject":[],"published":{"date-parts":[[2019,3,13]]}}}