{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T12:36:10Z","timestamp":1788957370796,"version":"build-2803163510"},"reference-count":21,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T00:00:00Z","timestamp":1650758400000},"content-version":"am","delay-in-days":345,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#am"},{"start":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T00:00:00Z","timestamp":1620950400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01CA215718"],"award-info":[{"award-number":["R01CA215718"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000005","name":"U.S. Department of Defense","doi-asserted-by":"publisher","award":["W81XWH\u201017\u20101\u20100438"],"award-info":[{"award-number":["W81XWH\u201017\u20101\u20100438"]}],"id":[{"id":"10.13039\/100000005","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000005","name":"U.S. Department of Defense","doi-asserted-by":"publisher","award":["W81XWH\u201017\u20101\u20100439"],"award-info":[{"award-number":["W81XWH\u201017\u20101\u20100439"]}],"id":[{"id":"10.13039\/100000005","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":[[2021,6]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Current prostate brachytherapy uses transrectal ultrasound images for implant guidance, where contours of the prostate and organs\u2010at\u2010risk are necessary for treatment planning and dose evaluation. This work aims to develop a deep learning\u2010based method for male pelvic multi\u2010organ segmentation on transrectal ultrasound images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We developed an anchor\u2010free mask convolutional neural network (CNN) that consists of three subnetworks, that is, a backbone, a fully convolutional one\u2010state object detector (FCOS), and a mask head. The backbone extracts multi\u2010level and multi\u2010scale features from an ultrasound (US) image. The FOCS utilizes these features to detect and label (classify) the volume\u2010of\u2010interests (VOIs) of organs. In contrast to the design of a previously investigated mask regional CNN (Mask R\u2010CNN), the FCOS is anchor\u2010free, which can capture the spatial correlation of multiple organs. The mask head performs segmentation on each detected VOI, where a spatial attention strategy is integrated into the mask head to focus on informative feature elements and suppress noise. For evaluation, we retrospectively investigated 83 prostate cancer patients by fivefold cross\u2010validation and a hold\u2010out test. The prostate, bladder, rectum, and urethra were segmented and compared with manual contours using the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD\n                      <jats:sub>95<\/jats:sub>\n                      ), mean surface distance (MSD), center of mass distance (CMD), and volume difference (VD).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The proposed method visually outperforms two competing methods, showing better agreement with manual contours and fewer misidentified speckles. In the cross\u2010validation study, the respective DSC and HD\n                      <jats:sub>95<\/jats:sub>\n                      results were as follows for each organ: bladder 0.75\u00a0\u00b1\u00a00.12, 2.58\u00a0\u00b1\u00a00.7\u00a0mm; prostate 0.93\u00a0\u00b1\u00a00.03, 2.28\u00a0\u00b1\u00a00.64\u00a0mm; rectum 0.90\u00a0\u00b1\u00a00.07, 1.65\u00a0\u00b1\u00a00.52\u00a0mm; and urethra 0.86\u00a0\u00b1\u00a00.07, 1.85\u00a0\u00b1\u00a01.71\u00a0mm. For the hold\u2010out tests, the DSC and HD\n                      <jats:sub>95<\/jats:sub>\n                      results were as follows: bladder 0.76\u00a0\u00b1\u00a00.13, 2.93\u00a0\u00b1\u00a01.29\u00a0mm; prostate 0.94\u00a0\u00b1\u00a00.03, 2.27\u00a0\u00b1\u00a00.79\u00a0mm; rectum 0.92\u00a0\u00b1\u00a00.03, 1.90\u00a0\u00b1\u00a00.28\u00a0mm; and urethra 0.85\u00a0\u00b1\u00a00.06, 1.81\u00a0\u00b1\u00a00.72\u00a0mm. Segmentation was performed in under 5\u00a0seconds.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed method demonstrated fast and accurate multi\u2010organ segmentation performance. It can expedite the contouring step of prostate brachytherapy and potentially enable auto\u2010planning and auto\u2010evaluation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.14895","type":"journal-article","created":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T13:31:19Z","timestamp":1619271079000},"page":"3055-3064","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Male pelvic multi\u2010organ segmentation on transrectal ultrasound using anchor\u2010free mask CNN"],"prefix":"10.1002","volume":"48","author":[{"given":"Yang","family":"Lei","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tonghe","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Roper","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashesh B.","family":"Jani","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sagar A.","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Walter J.","family":"Curran","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pretesh","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Winship Cancer Institute Emory University Atlanta GA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,5,14]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3006337"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3246613"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2968770"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14128"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aba410"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13577"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14134"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aba6d4"},{"key":"e_1_2_8_10_1","doi-asserted-by":"crossref","unstructured":"TianZ ShenC ChenH HeT.FCOS: Fully Convolutional One\u2010Stage Object Detection. Paper presented at: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV); 27 Oct.\u20132 Nov. 2019 2019.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"e_1_2_8_11_1","unstructured":"FabianI PaulJ JakobW et al.Inventors. batchgenerators \u2010 a python framework for data augmentation.2020."},{"key":"e_1_2_8_12_1","doi-asserted-by":"crossref","unstructured":"HeK ZhangX RenS SunJ.Deep residual learning for image recognition. Paper presented at: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 27\u201330 June 2016 2016.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14569"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13933"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2019.09.028"},{"key":"e_1_2_8_16_1","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek\u00d6 AbdulkadirA LienkampS BroxT RonnebergerO.3D U\u2010Net: Learning Dense Volumetric Segmentation from Sparse Annotation. Paper presented at: MICCAI2016.https:\/\/link.springer.com\/chapter\/10.1007\/978\u20103\u2010319\u201046723\u20108_49. Accessed October 02 2016.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13458"},{"key":"e_1_2_8_18_1","doi-asserted-by":"crossref","unstructured":"LeiY YaoJ HeX et al.Deep learning\u2010based breast tumor detection and segmentation in 3D ultrasound image. Paper presented at: Medical Imaging 2020: Ultrasonic Imaging and Tomography2020.https:\/\/doi.org\/10.1117\/12.2549157","DOI":"10.1117\/12.2549157"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14196"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab63bb"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brachy.2011.09.008"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14386"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.14895","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/mp.14895","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/am-pdf\/10.1002\/mp.14895","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.14895","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T15:09:22Z","timestamp":1692803362000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.14895"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,14]]},"references-count":21,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["10.1002\/mp.14895"],"URL":"https:\/\/doi.org\/10.1002\/mp.14895","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,14]]},"assertion":[{"value":"2020-11-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-04-06","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-05-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}