{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T17:27:53Z","timestamp":1793381273037,"version":"4.5.0"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"1153","license":[{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The segmentation of organs and structures is a critical component of radiation therapy planning, with manual segmentation being a laborious and time-consuming task. Interobserver variability can also impact the outcomes of radiation therapy. Deep neural networks have recently gained attention for their ability to automate segmentation tasks, with convolutional neural networks (CNNs) being a popular approach. This article provides a descriptive review of the literature on deep learning (DL) techniques for segmentation in radiation therapy planning. This review focuses on five clinical sub-sites and finds that U-net is the most commonly used CNN architecture. The studies using DL for image segmentation were included in brain, head and neck, lung, abdominal, and pelvic cancers. The majority of DL segmentation articles in radiation therapy planning have concentrated on normal tissue structures. N-fold cross-validation was commonly employed, without external validation. This research area is expanding quickly, and standardization of metrics and independent validation are critical to benchmarking and comparing proposed methods.<\/jats:p>","DOI":"10.1093\/bjr\/tqad018","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:13:44Z","timestamp":1704845624000},"page":"13-20","source":"Crossref","is-referenced-by-count":44,"title":["Deep learning for automated segmentation in radiotherapy: a narrative review"],"prefix":"10.1093","volume":"97","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1728-6776","authenticated-orcid":false,"given":"Jean-Emmanuel","family":"Bibault","sequence":"first","affiliation":[{"name":"Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique\u2014H\u00f4pitaux de Paris, Universit\u00e9 de Paris Cit\u00e9 , Paris, 75015, France"},{"name":"INSERM UMR 1138, Centre de Recherche des Cordeliers , Paris, 75006, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Giraud","sequence":"additional","affiliation":[{"name":"INSERM UMR 1138, Centre de Recherche des Cordeliers , Paris, 75006, France"},{"name":"Radiation Oncology Department, Piti\u00e9 Salp\u00eatri\u00e8re Hospital, Assistance Publique\u2014H\u00f4pitaux de Paris, Paris Sorbonne Universit\u00e9s , Paris, 75013, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,12,12]]},"reference":[{"issue":"4","key":"2024012406375242200_tqad018-B1","doi-asserted-by":"crossref","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","article-title":"Deep learning in medical imaging: general overview","volume":"18","author":"Lee","year":"2017","journal-title":"Korean J Radiol"},{"issue":"3","key":"2024012406375242200_tqad018-B2","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.radonc.2014.09.014","article-title":"Recommendations on how 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