{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T12:33:48Z","timestamp":1791376428204,"version":"4.3.2"},"reference-count":55,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Prostate cancer (PCa) is the second most common cancer in males worldwide, requiring improvements in diagnostic imaging to identify and treat it at an early stage. Bi-parametric magnetic resonance imaging (bpMRI) is recognized as an essential diagnostic technique for PCa, providing shorter acquisition times and cost-effectiveness. Nevertheless, accurate diagnosis using bpMRI images is difficult due to the inconspicuous and diverse characteristics of malignant tumors and the intricate structure of the prostate gland. An automated system is required to assist the medical professionals in accurate and early diagnosis with less effort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>This study recognizes the impact of zonal features on the advancement of the disease. The aim is to improve the diagnostic performance through a novel automated approach of a two-step mechanism using bpMRI images. First, pretraining a convolutional neural network (CNN)-based attention-guided U-Net model for segmenting the region of interest which is carried out in the prostate zone. Secondly, pretraining the same type of Attention U-Net is performed for lesion segmentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The performance of the pretrained models and training an attention-guided U-Net from the scratch for segmenting tumors on the prostate region is analyzed. The proposed attention-guided U-Net model achieved an area under the curve (AUC) of 0.85 and a dice similarity coefficient value of 0.82, outperforming some other pretrained deep learning models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our approach greatly enhances the identification and categorization of clinically significant PCa by including zonal data. Our approach exhibits exceptional performance in the accurate segmentation of bpMRI images compared to current techniques, as evidenced by thorough validation of a diverse dataset. This research not only enhances the field of medical imaging for oncology but also underscores the potential of deep learning models to progress PCa diagnosis and personalized patient care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076251314546","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T03:17:59Z","timestamp":1737688679000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["Enhancing prostate cancer segmentation in bpMRI: Integrating zonal awareness into attention-guided U-Net"],"prefix":"10.1177","volume":"11","author":[{"given":"Chao","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Urology, General Hospital of Northern Theater Command, Shenyang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Urology, General Hospital of Northern Theater Command, Shenyang, China"},{"name":"Department of Graduate School, China Medical University, Shenyang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yibo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nanomage Research Institute, Beijing, China"},{"name":"Gezhi AI Research Institute, Beijing, China"},{"name":"School of systems and Computing, University of New South Wales, Kensington, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianhui","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Urology, General Hospital of Northern Theater Command, Shenyang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"bibr1-20552076251314546","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2019.02.033"},{"key":"bibr2-20552076251314546","doi-asserted-by":"publisher","DOI":"10.1111\/bju.12892"},{"key":"bibr3-20552076251314546","doi-asserted-by":"publisher","DOI":"10.1016\/j.euf.2017.11.010"},{"key":"bibr4-20552076251314546","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2016152542"},{"key":"bibr5-20552076251314546","doi-asserted-by":"publisher","DOI":"10.1080\/21681805.2019.1675757"},{"key":"bibr6-20552076251314546","doi-asserted-by":"crossref","unstructured":"Lee CS, Nagy PG, Weaver SJ, et al. 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