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This study proposes a personalized auto\u2010segmentation (AS) framework to assist online delineation of prostate cancer using MRIgART.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Image data from 26 patients diagnosed with prostate cancer and treated using hypofractionated MRIgART (5 fractions per patient) were collected retrospectively. Daily pretreatment T2\u2010weighted MRI was performed using a 1.5\u2010T MRI system integrated into a Unity MR\u2010linac. First\u2010fraction image and contour data from 16 patients (80 image\u2010sets) were used to train the population AS model, and the remaining 10 patients composed the test set. The proposed personalized AS framework contained two main steps. First, a convolutional neural network was employed to train the population model using the training set. Second, for each test patient, the population model was progressively fine\u2010tuned with manually checked delineations of the patient's current and previous fractions to obtain a personalized model that was applied to the next fraction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Compared with the population model, the personalized models substantially improved the mean Dice similarity coefficient from 0.79 to 0.93 for the prostate clinical target volume (CTV), 0.91 to 0.97 for the bladder, 0.82 to 0.92 for the rectum, and 0.91 to 0.93 for the femoral heads, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed method can achieve accurate segmentation and potentially shorten the overall online delineation time of MRIgART.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15793","type":"journal-article","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T07:33:50Z","timestamp":1654587230000},"page":"4971-4979","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Personalized auto\u2010segmentation for magnetic resonance imaging\u2013guided adaptive radiotherapy of prostate cancer"],"prefix":"10.1002","volume":"49","author":[{"given":"Xinyuan","family":"Chen","sequence":"first","affiliation":[{"name":"National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College  Beijing China"},{"name":"National Cancer Center\/National Clinical Research Center for Cancer\/Hebei Cancer Hospital Chinese Academy of Medical Sciences  Langfang 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