{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:40:08Z","timestamp":1773805208942,"version":"3.50.1"},"reference-count":26,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T00:00:00Z","timestamp":1603152000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011744","name":"National University Health System","doi-asserted-by":"publisher","award":["NUHSCGSF\/2019\/07"],"award-info":[{"award-number":["NUHSCGSF\/2019\/07"]}],"id":[{"id":"10.13039\/501100011744","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2020,10,20]]},"abstract":"<jats:p>Prostate segmentation in multiparametric magnetic resonance imaging (mpMRI) can help to support prostate cancer diagnosis and therapy treatment. However, manual segmentation of the prostate is subjective and time-consuming. Many deep learning monomodal networks have been developed for automatic whole prostate segmentation from T2-weighted MR images. We aimed to investigate the added value of multimodal networks in segmenting the prostate into the peripheral zone (PZ) and central gland (CG). We optimized and evaluated monomodal DenseVNet, multimodal ScaleNet, and monomodal and multimodal HighRes3DNet, which yielded dice score coefficients (DSC) of 0.875, 0.848, 0.858, and 0.890 in WG, respectively. Multimodal HighRes3DNet and ScaleNet yielded higher DSC with statistical differences in PZ and CG only compared to monomodal DenseVNet, indicating that multimodal networks added value by generating better segmentation between PZ and CG regions but did not improve the WG segmentation. No significant difference was observed in the apex and base of WG segmentation between monomodal and multimodal networks, indicating that the segmentations at the apex and base were more affected by the general network architecture. The number of training data was also varied for DenseVNet and HighRes3DNet, from 20 to 120 in steps of 20. DenseVNet was able to yield DSC of higher than 0.65 even for special cases, such as TURP or abnormal prostate, whereas HighRes3DNet\u2019s performance fluctuated with no trend despite being the best network overall. Multimodal networks did not add value in segmenting special cases but generally reduced variations in segmentation compared to the same matched monomodal network.<\/jats:p>","DOI":"10.1155\/2020\/8861035","type":"journal-article","created":{"date-parts":[[2020,10,21]],"date-time":"2020-10-21T23:50:05Z","timestamp":1603324205000},"page":"1-12","source":"Crossref","is-referenced-by-count":19,"title":["Evaluation of Multimodal Algorithms for the Segmentation of Multiparametric MRI Prostate Images"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6634-8785","authenticated-orcid":true,"given":"Ying-Hwey","family":"Nai","sequence":"first","affiliation":[{"name":"Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernice W.","family":"Teo","sequence":"additional","affiliation":[{"name":"Nanyang Junior College, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nadya L.","family":"Tan","sequence":"additional","affiliation":[{"name":"St. Joseph\u2019s Institution International, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Koby Yi Wei","family":"Chua","sequence":"additional","affiliation":[{"name":"Anglo-Chinese Independent, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun Kit","family":"Wong","sequence":"additional","affiliation":[{"name":"Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sophie","family":"O\u2019Doherty","sequence":"additional","affiliation":[{"name":"Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mary C.","family":"Stephenson","sequence":"additional","affiliation":[{"name":"Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Josh","family":"Schaefferkoetter","sequence":"additional","affiliation":[{"name":"Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore"},{"name":"Joint Department of Medical Imaging, University Health Network, Toronto, Canada"},{"name":"Siemens Medical Solutions USA, Inc., Molecular Imaging, Knoxville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yee Liang","family":"Thian","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, National University Hospital, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edmund","family":"Chiong","sequence":"additional","affiliation":[{"name":"Department of Surgery (Urology), Yong Loo Lin School of Medicine, National University of Singapore, Singapore"},{"name":"Department of Urology, National University Hospital, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9997-9540","authenticated-orcid":true,"given":"Anthonin","family":"Reilhac","sequence":"additional","affiliation":[{"name":"Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"issue":"1","key":"2","first-page":"8","article-title":"An evaluation of usefulness of prostate specific antigen and digital rectal examination in the diagnosis of prostate cancer in an unscreened population: experience in a Nigerian teaching hospital","volume":"32","author":"R. 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