{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T03:02:03Z","timestamp":1789700523748,"version":"4.0.1"},"reference-count":26,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:00:00Z","timestamp":1787011200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:00:00Z","timestamp":1787011200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate delineation of the prostate and surrounding organs\u2010at\u2010risk (OARs) is essential for HDR prostate brachytherapy. Manual contouring on post\u2010catheter CT images is time\u2010consuming and prone to variability due to artifacts and anatomical deformation from the implanted brachytherapy catheters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop and validate a 3D deep learning autosegmentation model for prostate and OAR contouring on post\u2010catheter CT images for prostate brachytherapy planning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A self\u2010configuring U\u2010Net architecture (nnU\u2010Net) was trained on 206 HDR prostate brachytherapy cases and tested on an additional 36 patients. Structures included prostate PTV, bladder, rectum, and urethra identified from a Foley catheter. Performance was evaluated against a commercially available model using geometric (Dice coefficient (DSC), Hausdorff distance (HD95%), average surface distance (ASD)) and dosimetric metrics (PTV V100%, bladder and rectum D1cc, urethra D0.1cc). Statistical significance was assessed using paired Wilcoxon signed\u2010rank tests.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      NnU\u2010Net achieved superior geometric accuracy versus the commercial model for all structures within slices containing the prostate. For prostate PTV, nnU\u2010Net yielded DSC\u00a0=\u00a00.91 +\/\u2212 0.04, HD95%\u00a0=\u00a03.62 +\/\u2212 2.23\u00a0mm, and ASD\u00a0=\u00a01.25 +\/\u2212 0.66\u00a0mm, compared to DSC\u00a0=\u00a00.78 +\/\u2212 0.07, HD95%\u00a0=\u00a09.52 +\/\u2212 4.73\u00a0mm, and ASD\u00a0=\u00a02.97 +\/\u2212 0.90\u00a0mm for the commercial model. Urethra segmentation was only provided by nnU\u2010Net (DSC\u00a0=\u00a00.84 +\/\u2212 0.09). Dosimetric differences for nnU\u2010Net were clinically negligible: \u0394PTV V100%\u00a0=\u00a0\u22120.25 +\/\u22122.30%, bladder \u0394D1cc\u00a0=\u00a00.13 +\/\u2212 0.75\u00a0Gy, rectum \u0394D1cc\u00a0=\u00a00.34 +\/\u2212 0.74\u00a0Gy, and urethra \u0394D0.1cc\u00a0=\u00a0\u22120.03 +\/\u2212 0.16\u00a0Gy. Differences in PTV V100% were not significant for nnU\u2010Net (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.91) but significant for the commercial model (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A brachytherapy\u2010specific nnU\u2010Net model enables accurate autosegmentation of prostate and OARs on post\u2010catheter CT images, outperforming an EBRT\u2010trained commercial solution. Minimal dosimetric differences support clinical feasibility and potential workflow improvements in HDR prostate brachytherapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70748","type":"journal-article","created":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T05:04:38Z","timestamp":1787115878000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning auto\u2010contouring of target and organs\u2010at\u2010risk on post\u2010catheter implant CT images for prostate HDR brachytherapy"],"prefix":"10.1002","volume":"27","author":[{"given":"Eric M.","family":"Wallat","sequence":"first","affiliation":[{"name":"Department of Radiation Medicine University of Wisconsin\u2013Madison  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph B.","family":"Schulz","sequence":"additional","affiliation":[{"name":"Department of Medical Physics University of Wisconsin\u2013Madison  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John M.","family":"Floberg","sequence":"additional","affiliation":[{"name":"Department of Radiation Medicine University of Wisconsin\u2013Madison  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory","family":"Cooley","sequence":"additional","affiliation":[{"name":"Department of Radiation Medicine University of Wisconsin\u2013Madison  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bryan P.","family":"Bednarz","sequence":"additional","affiliation":[{"name":"Department of Medical Physics University of Wisconsin\u2013Madison  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jordan M.","family":"Slagowski","sequence":"additional","affiliation":[{"name":"Department of Radiation Medicine University of Wisconsin\u2013Madison  Madison Wisconsin USA"},{"name":"Department of Medical Physics University of Wisconsin\u2013Madison  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,18]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.70028"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2017.01.008"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brachy.2025.06.005"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.21037\/tau.2017.12.08"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167\u20108140(98)00021\u20108"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.70034"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014\u2010022\u201002121\u20103"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.14371"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14134"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2024.07.2054"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16716"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106752"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16811"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11548\u2010020\u201002231\u2010x"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.70089"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592\u2010020\u201001008\u2010z"},{"key":"e_1_2_9_18_1","volume-title":"Automated Contouring Using Neural Networks","author":"Wan H","year":"2024"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12880\u2010015\u20100068\u2010x"},{"key":"e_1_2_9_20_1","unstructured":"CardosoMJ LiW BrownR et\u00a0al.MONAI: an open\u2010source framework for deep learning in healthcare.arXiv. 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