{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T03:01:27Z","timestamp":1791255687500,"version":"4.1.0"},"reference-count":42,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:00:00Z","timestamp":1700524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2021R1A2C2005824"],"award-info":[{"award-number":["2021R1A2C2005824"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["6-2020-0087"],"award-info":[{"award-number":["6-2020-0087"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Yonsei University College of Medicine","award":["2021R1A2C2005824"],"award-info":[{"award-number":["2021R1A2C2005824"]}]},{"name":"Yonsei University College of Medicine","award":["6-2020-0087"],"award-info":[{"award-number":["6-2020-0087"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>U-Net, based on a deep convolutional network (CNN), has been clinically used to auto-segment normal organs, while still being limited to the planning target volume (PTV) segmentation. This work aims to address the problems in two aspects: 1) apply one of the newest network architectures such as vision transformers other than the CNN-based networks, and 2) find an appropriate combination of network hyper-parameters with reference to recently proposed nnU-Net (\u201cno-new-Net\u201d). VT U-Net was adopted for auto-segmenting the whole pelvis prostate PTV as it consisted of fully transformer architecture. The upgraded version (v.2) applied the nnU-Net-like hyper-parameter optimizations, which did not fully cover the transformer-oriented hyper-parameters. Thus, we tried to find a suitable combination of two key hyper-parameters (patch size and embedded dimension) for 140 CT scans throughout 4-fold cross validation. The VT U-Net v.2 with hyper-parameter tuning yielded the highest dice similarity coefficient (DSC) of 82.5 and the lowest 95% Haussdorff distance (HD95) of 3.5 on average among the seven recently proposed deep learning networks. Importantly, the nnU-Net with hyper-parameter optimization achieved competitive performance, although this was based on the convolution layers. The network hyper-parameter tuning was demonstrated to be necessary even for the newly developed architecture of vision transformers.<\/jats:p>","DOI":"10.3390\/cancers15235507","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T12:12:13Z","timestamp":1700568733000},"page":"5507","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Empowering Vision Transformer by Network Hyper-Parameter Selection for Whole Pelvis Prostate Planning Target Volume Auto-Segmentation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1825-815X","authenticated-orcid":false,"given":"Hyeonjeong","family":"Cho","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea"},{"name":"Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Sung","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin Sung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9435-7750","authenticated-orcid":false,"given":"Woong Sub","family":"Koom","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4652-8682","authenticated-orcid":false,"given":"Hojin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"ref_1","unstructured":"Kainm\u00fcller, D., Lange, T., and Lamecker, H. 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