{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T01:20:01Z","timestamp":1772760001231,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T00:00:00Z","timestamp":1618185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011912","name":"Taipei Veterans General Hospital","doi-asserted-by":"publisher","award":["V110C-182"],"award-info":[{"award-number":["V110C-182"]}],"id":[{"id":"10.13039\/501100011912","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The accuracy in diagnosing prostate cancer (PCa) has increased with the development of multiparametric magnetic resonance imaging (mpMRI). Biparametric magnetic resonance imaging (bpMRI) was found to have a diagnostic accuracy comparable to mpMRI in detecting PCa. However, prostate MRI assessment relies on human experts and specialized training with considerable inter-reader variability. Deep learning may be a more robust approach for prostate MRI assessment. Here we present a method for autosegmenting the prostate zone and cancer region by using SegNet, a deep convolution neural network (DCNN) model. We used PROSTATEx dataset to train the model and combined different sequences into three channels of a single image. For each subject, all slices that contained the transition zone (TZ), peripheral zone (PZ), and PCa region were selected. The datasets were produced using different combinations of images, including T2-weighted (T2W) images, diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) images. Among these groups, the T2W + DWI + ADC images exhibited the best performance with a dice similarity coefficient of 90.45% for the TZ, 70.04% for the PZ, and 52.73% for the PCa region. Image sequence analysis with a DCNN model has the potential to assist PCa diagnosis.<\/jats:p>","DOI":"10.3390\/s21082709","type":"journal-article","created":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T11:05:06Z","timestamp":1618225506000},"page":"2709","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6846-5352","authenticated-orcid":false,"given":"Chih-Ching","family":"Lai","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 300044, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hsin-Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Taipei Veterans General Hospital, Taipei 112201, Taiwan"},{"name":"School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fu-Nien","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 300044, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Ching","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan"},{"name":"Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei 112201, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tzu-Ping","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan"},{"name":"Department of Urology, Taipei Veterans General Hospital, Taipei 112201, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6318-1377","authenticated-orcid":false,"given":"Hsu-Hsia","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 300044, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2776-4671","authenticated-orcid":false,"given":"Shu-Huei","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Radiology, Taipei Veterans General Hospital, Taipei 112201, Taiwan"},{"name":"School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,12]]},"reference":[{"key":"ref_1","unstructured":"(2021, February 06). 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