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Magnetic resonance imaging (MRI) is used as a standard imaging modality for CTV segmentation in cervical cancer due to its superior soft\u2010tissue contrast. However, the delineation of CTV is challenging as CTV contains microscopic extensions that are not clearly visible even in MR images, resulting in significant contour variability among radiation oncologists depending on their knowledge and experience. In this study, we propose a fully automated deep learning\u2013based method to segment CTV from MR images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Our method begins with the bladder segmentation, from which the CTV position is estimated in the axial view. The superior\u2013inferior CTV span is then detected using an Attention U\u2010Net. A CTV\u2010specific region of interest (ROI) is determined, and three\u2010dimensional (3\u2010D) blocks are extracted from the ROI volume. Finally, a CTV segmentation map is computed using a 3\u2010D U\u2010Net from the extracted 3\u2010D blocks.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We developed and evaluated our method using 213 MRI scans obtained from 125 patients (183 for training, 30 for test). Our method achieved (mean \u00b1 SD) Dice similarity coefficient of 0.85 \u00b1 0.03 and the 95th percentile Hausdorff distance of 3.70 \u00b1 0.35\u00a0mm on test cases, outperforming other state\u2010of\u2010the\u2010art methods significantly (<jats:italic>p<\/jats:italic>\u2010value\u00a0&lt;\u00a00.05). Our method also produces an uncertainty map along with the CTV segmentation by employing the Monte Carlo dropout technique to draw physician's attention to the regions with high uncertainty, where careful review and manual correction may be needed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Experimental results show that the developed method is accurate, fast, and reproducible for contouring CTV from MRI, demonstrating its potential to assist radiation oncologists in alleviating the burden of tedious contouring for RT planning in cervical cancer.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.13725","type":"journal-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T13:46:04Z","timestamp":1658929564000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Fully automated segmentation of clinical target volume in cervical cancer from magnetic resonance imaging with convolutional neural network"],"prefix":"10.1002","volume":"23","author":[{"given":"Fatemeh","family":"Zabihollahy","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology and Molecular Radiation Sciences Johns Hopkins University School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akila N.","family":"Viswanathan","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Molecular Radiation Sciences Johns Hopkins University School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ehud J.","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Division of Cardiology, Department of Medicine Johns Hopkins University School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junghoon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Molecular Radiation Sciences Johns Hopkins University School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.5114\/jcb.2018.75595"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2010.10.004"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.12120315"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1102\/1470\u20107330.2004.0054"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2015.10.007"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2009.10.075"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2008.10.023"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2015.04.006"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/54\/23\/007"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2009.2016560"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2012.2186976"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2012.12.006"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12602"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102101"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.581347"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101909"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14733"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2020.09.060"},{"key":"e_1_2_9_20_1","doi-asserted-by":"crossref","unstructured":"HeK ZhangX RenS SunJ.Deep residual learning for image recognition. 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