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However, even in magnetic resonance\u2010guided radiation therapy (MRgRT), consistent delineation of the urethra is challenging, particularly in online adaptive radiotherapy. This paper presented a fully automatic MRgRT\u2010based prostatic urethra segmentation framework.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Twenty\u2010eight prostate cancer patients were included in this study. In\u2010house 3D half fourier single\u2010shot turbo spin\u2010echo (HASTE) and turbo spin echo (TSE) sequences were used to image the Foley\u2010free urethra on a 0.35\u00a0T MRgRT system. The segmentation pipeline uses 3D nnU\u2010Net as the base and innovatively combines ground truth and its corresponding radial distance (RD) map during training supervision. Additionally, we evaluate the benefit of incorporating a convolutional long short term memory (LSTM\u2010Conv) layer and spatial recurrent convolution layer (RCL) into nnU\u2010Net. A novel slice\u2010by\u2010slice simple exponential smoothing (SEPS) method specifically for tubular structures was used to post\u2010process the segmentation results.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The experimental results show that nnU\u2010Net trained using a combination of Dice, cross\u2010entropy and RD achieved a Dice score of 77.1\u00a0\u00b1\u00a02.3% in the testing dataset. With SEPS, Hausdorff distance (HD) and 95% HD were reduced to 2.95\u00a0\u00b1\u00a00.17\u00a0mm and 1.84\u00a0\u00b1\u00a00.11\u00a0mm, respectively. LSTM\u2010Conv and RCL layers only minimally improved the segmentation precision.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We present the first Foley\u2010free MRgRT\u2010based automated urethra segmentation study. Our method is built on a data\u2010driven neural network with novel cost functions and a post\u2010processing step designed for tubular structures. The performance is consistent with the need for online and offline urethra dose reduction in prostate radiotherapy.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15983","type":"journal-article","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T11:13:48Z","timestamp":1663240428000},"page":"354-364","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Fully automated segmentation of prostatic urethra for MR\u2010guided radiation therapy"],"prefix":"10.1002","volume":"50","author":[{"given":"Di","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Computer Science University of California  Los Angeles California USA"},{"name":"Department of Radiation Oncology University of California  Los Angeles California USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting Martin","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of 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