{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:31:25Z","timestamp":1762507885796},"reference-count":32,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T00:00:00Z","timestamp":1572480000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100007210","name":"Varian Medical Systems","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007210","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007052","name":"Memorial Sloan-Kettering Cancer Center","doi-asserted-by":"publisher","award":["P30 CA008748"],"award-info":[{"award-number":["P30 CA008748"]}],"id":[{"id":"10.13039\/100007052","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>Manual delineation of head and neck (H&amp;N) organ\u2010at\u2010risk (OAR) structures for radiation therapy planning is time consuming and highly variable. Therefore, we developed a dynamic multiatlas selection\u2010based approach for fast and reproducible segmentation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Our approach dynamically selects and weights the appropriate number of atlases for weighted label fusion and generates segmentations and consensus maps indicating voxel\u2010wise agreement between different atlases. Atlases were selected for a target as those exceeding an alignment weight called dynamic atlas attention index. Alignment weights were computed at the image level and called global weighted voting (GWV) or at the structure level and called structure weighted voting (SWV) by using a normalized metric computed as the sum of squared distances of computed tomography (CT)\u2010radiodensity and modality\u2010independent neighborhood descriptors (extracting edge information). Performance comparisons were performed using 77 H&amp;N CT images from an internal Memorial Sloan\u2010Kettering Cancer Center dataset (N\u00a0=\u00a045) and an external dataset (N\u00a0=\u00a032) using Dice similarity coefficient (DSC), Hausdorff distance (HD), 95th percentile of HD, median of maximum surface distance, and volume ratio error against expert delineation. Pairwise DSC accuracy comparisons of proposed (GWV, SWV) vs single best atlas (BA) or majority voting (MV) methods were performed using Wilcoxon rank\u2010sum tests.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Both SWV and GWV methods produced significantly better segmentation accuracy than BA (<jats:italic>P<\/jats:italic>\u00a0&lt;\u00a00.001) and MV (<jats:italic>P<\/jats:italic>\u00a0&lt;\u00a00.001) for all OARs within both datasets. SWV generated the most accurate segmentations with DSC of: 0.88 for oral cavity, 0.85 for mandible, 0.84 for cord, 0.76 for brainstem and parotids, 0.71 for larynx, and 0.60 for submandibular glands. SWV\u2019s accuracy exceeded GWV's for submandibular glands (DSC\u00a0=\u00a00.60 vs 0.52, <jats:italic>P<\/jats:italic>\u00a0=\u00a00.019).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The contributed SWV and GWV methods generated more accurate automated segmentations than the other two multiatlas\u2010based segmentation techniques. The consensus maps could be combined with segmentations to visualize voxel\u2010wise consensus between atlases within OARs during manual review.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.13854","type":"journal-article","created":{"date-parts":[[2019,10,6]],"date-time":"2019-10-06T09:22:56Z","timestamp":1570353776000},"page":"5612-5622","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Dynamic multiatlas selection\u2010based consensus segmentation of head and neck structures from CT images"],"prefix":"10.1002","volume":"46","author":[{"given":"Rabia","family":"Haq","sequence":"first","affiliation":[{"name":"Department of Medical Physics Memorial Sloan\u2010Kettering Cancer Center New York NY 10065 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sean L.","family":"Berry","sequence":"additional","affiliation":[{"name":"Department of Medical Physics Memorial Sloan\u2010Kettering Cancer Center New York NY 10065 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph O.","family":"Deasy","sequence":"additional","affiliation":[{"name":"Department of Medical Physics Memorial Sloan\u2010Kettering Cancer Center New York NY 10065 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Margie","family":"Hunt","sequence":"additional","affiliation":[{"name":"Department of Medical Physics Memorial Sloan\u2010Kettering Cancer Center New York NY 10065 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harini","family":"Veeraraghavan","sequence":"additional","affiliation":[{"name":"Department of Medical Physics Memorial Sloan\u2010Kettering Cancer Center New York NY 10065 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,10,31]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2008.10.034"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2010.10.019"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12197"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4871623"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2014.08.350"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12045"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2003.11.010"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.05.061"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2009.02.018"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.07.050"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4927567"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2004.828354"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2364863"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2009.09.069"},{"key":"e_1_2_7_16_1","doi-asserted-by":"crossref","unstructured":"HaoY LiuJ DuanYY et al.Local label learning (L3) for multi\u2010atlas based segmentation. 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