{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T17:58:02Z","timestamp":1730311082524,"version":"3.28.0"},"reference-count":27,"publisher":"SPIE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,4]]},"DOI":"10.1117\/12.2609406","type":"proceedings-article","created":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T22:23:22Z","timestamp":1648765402000},"page":"56","source":"Crossref","is-referenced-by-count":0,"title":["Parotid gland segmentation with nnU-Net: deployment scenario and inter-observer variability analysis"],"prefix":"10.1117","author":[{"given":"Ga\u0161per","family":"Podobnik","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Primo\u017e","family":"Strojan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Primo\u017e","family":"Peterlin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bulat","family":"Ibragimov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toma\u017e","family":"Vrtovec","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"189","reference":[{"doi-asserted-by":"publisher","key":"c1","DOI":"10.1016\/S0140-6736(08)60728-X"},{"doi-asserted-by":"publisher","key":"c2","DOI":"10.1016\/j.radonc.2019.09.022"},{"doi-asserted-by":"publisher","key":"c3","DOI":"10.1016\/j.radonc.2019.05.010"},{"key":"c4","first-page":"223","article-title":"Clinical implementation of deepvoxnet for auto-delineation of organs at risk in head and neck cancer patients in radiotherapy","volume-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","volume":"11041","author":"Willems","year":"2018"},{"key":"c5","first-page":"519","article-title":"Segmentation of head-and-neck organs-at-risk in longitudinal CT scans combining deformable registrations and convolutional neural networks","volume":"8","author":"Vandewinckele","year":"2020","journal-title":"Comput Methods Biomech Biomed Engin: Imaging and Visualization"},{"doi-asserted-by":"publisher","key":"c6","DOI":"10.1016\/j.radonc.2019.10.019"},{"doi-asserted-by":"publisher","key":"c7","DOI":"10.1002\/mp.v46.11"},{"key":"c8","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","volume-title":"Proceedings of MICCAI 2015","volume":"9351","author":"Ronneberger","year":"2015"},{"doi-asserted-by":"publisher","key":"c9","DOI":"10.1002\/mp.2019.46.issue-6"},{"doi-asserted-by":"publisher","key":"c10","DOI":"10.1016\/j.ijrobp.2020.07.814"},{"doi-asserted-by":"publisher","key":"c11","DOI":"10.1088\/1361-6560\/abd953"},{"doi-asserted-by":"publisher","key":"c12","DOI":"10.1016\/j.media.2016.10.004"},{"doi-asserted-by":"publisher","key":"c13","DOI":"10.1038\/s41592-020-01008-z"},{"doi-asserted-by":"publisher","key":"c14","DOI":"10.1002\/mp.12197"},{"key":"c15","first-page":"1","article-title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"Aerts","year":"2014","journal-title":"Nat. Commun"},{"key":"c16","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1109\/CVPR.2016.90","article-title":"Deep Residual Learning for Image Recognition","volume-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"He","year":"2016"},{"doi-asserted-by":"publisher","key":"c17","DOI":"10.1002\/mp.v47.9"},{"key":"c18","first-page":"158","article-title":"Deep neural networks for fast segmentation of 3D medical images","volume-title":"Proceedings of MICCAI 2016","volume":"9901","author":"Fritscher","year":"2016"},{"doi-asserted-by":"publisher","key":"c19","DOI":"10.1117\/1.JMI.6.1.011005"},{"key":"c20","article-title":"Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy","author":"Nikolov","year":"2018","journal-title":"arXiv"},{"doi-asserted-by":"publisher","key":"c21","DOI":"10.1002\/mp.2018.45.issue-10"},{"doi-asserted-by":"publisher","key":"c22","DOI":"10.1002\/mp.2019.46.issue-2"},{"doi-asserted-by":"publisher","key":"c23","DOI":"10.1038\/s42256-019-0099-z"},{"doi-asserted-by":"publisher","key":"c24","DOI":"10.1007\/s11548-019-01922-4"},{"doi-asserted-by":"publisher","key":"c25","DOI":"10.1088\/1361-6560\/ab79c3"},{"doi-asserted-by":"publisher","key":"c26","DOI":"10.1016\/j.media.2020.101831"},{"key":"c27","article-title":"Nested-block self-attention for robust radiotherapy planning segmentation","author":"Veeraraghavan","year":"2021","journal-title":"arXiv"}],"event":{"name":"Image Processing","start":{"date-parts":[[2022,2,20]]},"location":"San Diego, United States","end":{"date-parts":[[2022,3,28]]}},"container-title":["Medical Imaging 2022: Image Processing"],"original-title":[],"deposited":{"date-parts":[[2022,7,3]],"date-time":"2022-07-03T01:26:56Z","timestamp":1656811616000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/12032\/2609406\/Parotid-gland-segmentation-with-nnU-Net--deployment-scenario-and\/10.1117\/12.2609406.full"}},"subtitle":[],"editor":[{"given":"Ivana","family":"I\u0161gum","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]},{"given":"Olivier","family":"Colliot","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,4,4]]},"references-count":27,"URL":"https:\/\/doi.org\/10.1117\/12.2609406","relation":{},"subject":[],"published":{"date-parts":[[2022,4,4]]}}}