{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:12:27Z","timestamp":1792080747142,"version":"4.4.0"},"reference-count":41,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003246","name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","award":["639.073.506"],"award-info":[{"award-number":["639.073.506"]}],"id":[{"id":"10.13039\/501100003246","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":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Deep learning\u2010based unsupervised image registration has recently been proposed, promising fast registration. However, it has yet to be adopted in the online adaptive magnetic resonance imaging\u2010guided radiotherapy (MRgRT)\u00a0workflow.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>In this paper, we design an unsupervised, joint rigid, and deformable registration framework for contour propagation in MRgRT of prostate\u00a0cancer.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Three\u2010dimensional pelvic T2\u2010weighted MRIs of 143 prostate cancer patients undergoing radiotherapy were collected and divided into 110, 13, and 20 patients for training, validation, and testing. We designed a framework using convolutional neural networks (CNNs) for rigid and deformable registration. We selected the deformable registration network architecture among U\u2010Net, MS\u2010D Net, and LapIRN and optimized the training strategy (end\u2010to\u2010end vs. sequential). The framework was compared against an iterative baseline registration. We evaluated registration accuracy (the Dice and Hausdorff distance of the prostate and bladder contours), structural similarity index, and folding percentage to compare the methods. We also evaluated the framework's robustness to rigid and elastic deformations and bias field\u00a0perturbations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The end\u2010to\u2010end trained framework comprising LapIRN for the deformable component achieved the best median (interquartile range) prostate and bladder Dice of\u00a00.89 (0.85\u20130.91) and 0.86 (0.80\u20130.91), respectively. This accuracy was comparable to the iterative baseline registration: prostate and bladder Dice of 0.91 (0.88\u20130.93) and 0.86 (0.80\u20130.92). The best models complete rigid and deformable registration in 0.002 (0.0005) and 0.74 (0.43) s (Nvidia Tesla V100\u2010PCIe 32 GB GPU), respectively. We found that the models are robust to translations up to 52 mm, rotations up to 15, elastic deformations up to 40 mm, and bias\u00a0fields.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our proposed unsupervised, deep learning\u2010based registration framework can perform rigid and deformable registration in less than a second with contour propagation accuracy comparable with iterative registration.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.17000","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T18:08:57Z","timestamp":1708970937000},"page":"2367-2377","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep\u2010learning\u2010based joint rigid and deformable contour propagation for magnetic resonance imaging\u2010guided prostate radiotherapy"],"prefix":"10.1002","volume":"51","author":[{"given":"Iris D.","family":"Kolenbrander","sequence":"first","affiliation":[{"name":"Medical Image Analysis Group, Department of Biomedical Engineering Eindhoven University of Technology Eindhoven The Netherlands"},{"name":"Eindhoven Artificial Intelligence Systems Institute Eindhoven University of Technology Eindhoven The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matteo","family":"Maspero","sequence":"additional","affiliation":[{"name":"Computational Imaging Group for MR Diagnostics &amp; Therapy Center for Image Sciences University Medical Center Utrecht Utrecht The Netherlands"},{"name":"Department of Radiotherapy University Medical Center Utrecht Utrecht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Allard A.","family":"Hendriksen","sequence":"additional","affiliation":[{"name":"Computational Imaging Centrum Wiskunde &amp; Informatica Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan","family":"Pollitt","sequence":"additional","affiliation":[{"name":"Medical Image Analysis Group, Department of Biomedical Engineering Eindhoven University of Technology Eindhoven The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jochem R. 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