{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T10:56:06Z","timestamp":1769856966812,"version":"3.49.0"},"reference-count":23,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T00:00:00Z","timestamp":1564099200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100011676","name":"Elekta","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100011676","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":[[2019,9]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>Evaluation of contour accuracy in radiation therapy planning requires manual interaction and is one of the most limiting bottlenecks for online replanning. This study aims to develop an automatic approach to rapidly evaluate contour quality based on image texture features to facilitate the routine practice of online adaptive replanning (OLAR).<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>Fifty\u2010five pancreas cancer patients were selected from a clinical database of patients treated at our institution from 2011 to 2018. For each patient, the pancreas head and duodenum were contoured in five images (one fraction per week) resulting in a total of 275 CT image sets with corresponding ground\u2010truth contours. A second set of inaccurate contours was generated using deformable\u2010image\u2010registration\u2010based contour propagation. Three subregions, core, inner shell and outer shell, were generated from the contour of each organ. Texture features were extracted from each subregion and descriptive features of each subregion were identified using the image set with corresponding ground\u2010truth contours. A three\u2010level decision tree model was constructed based on texture constraints empirically determined for the three subregions. The two datasets containing ground truth and inaccurate contours were merged. Randomized threefold cross\u2010validation was performed and repeated three times.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The first level of the decision tree utilizes textures derived from principal component analysis of a subset of extracted features from the core subregion (five PCs for pancreas head, seven PCs for duodenum). The second and third levels of the decision tree use gray\u2010level co\u2010occurrence matrix (GLCM)\u2010based cluster prominence to reject inaccurate contours. The trained model identifies accurate and inaccurate contours with an average sensitivity\/specificity of 85%\/91% for the pancreas head and 92%\/92% for the duodenum contours. The false\u2010positive rate is 9% and 8% for pancreas head and duodenum, respectively. The execution time is less than 15\u00a0s using a standard desktop computer.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Quantitative image features can be used to develop a model to rapidly validate the quality of an organ contour. Our model accurately classifies unseen contours as accurate or inaccurate with high sensitivity and specificity. As auto\u2010segmentation continues to improve in quality and accuracy, this method may be integrated into a fully automatic pipeline for auto\u2010segmentation, contour\u2010quality evaluation and contour correction, which would replace the time\u2010consuming manual review process, thereby facilitating the more routine practice of OLAR.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.13697","type":"journal-article","created":{"date-parts":[[2019,7,5]],"date-time":"2019-07-05T10:01:24Z","timestamp":1562320884000},"page":"4010-4020","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Texture\u2010based, automatic contour validation for online adaptive replanning: A feasibility study on abdominal organs"],"prefix":"10.1002","volume":"46","author":[{"given":"Ying","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Medical College of Wisconsin WI 53226 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tia E.","family":"Plautz","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Medical College of Wisconsin WI 53226 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Hao","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Medical College of Wisconsin WI 53226 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Catherine","family":"Kinchen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Medical College of Wisconsin WI 53226 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"X. Allen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Medical College of Wisconsin WI 53226 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,7,26]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/42\/1\/008"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1201\/b10517"},{"key":"e_1_2_9_4_1","first-page":"e1618","article-title":"Online adaptive radiation therapy: implementation of a new process of care","volume":"9","author":"Lamb J","year":"2017","journal-title":"Cureus"},{"key":"e_1_2_9_5_1","first-page":"1","article-title":"Accuracy of deformable image registration for contour propagation in adaptive lung radiotherapy","volume":"8","author":"Nicholas H","year":"2013","journal-title":"Radiat Oncol"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4901409"},{"key":"e_1_2_9_7_1","doi-asserted-by":"crossref","unstructured":"CommandeurF AcostaO SimonA MathieuR.HaigronP CrevoisierRD.Propagation of the MRI prostate delineation to the planning CT: A new matching contour framework. 2013.","DOI":"10.1109\/ISBI.2013.6556521"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/61\/24\/8577"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/60\/13\/5199"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4906197"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2013.2251421"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12835"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1973.4309314"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12256"},{"key":"e_1_2_9_15_1","unstructured":"TomasiC ManduchiR.Bilateral filtering for gray and color images. 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