{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T09:58:59Z","timestamp":1790330339570,"version":"4.1.0"},"reference-count":38,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"3","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Deep learning has significantly improved medical image segmentation, particularly in complex applications such as gynecological brachytherapy (BT). This study proposes a novel U-Net variant, the residual in residual dense block U-Net (RU-Net), to address the segmentation of organs-at-risk (OARs), which are often distorted due to BT probe insertion. Accurate segmentation is essential for effective treatment planning; however, task-specific datasets are scarce. To tackle this, a dedicated magnetic resonance imaging (MRI) dataset was developed, focusing on the precise delineation of OARs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>The RU-Net architecture enhances feature extraction through embedded residual in residual dence block (RRDB) modules. It was trained and evaluated using a multiplanar (axial, coronal, and sagittal) MRI dataset. The study benchmarks RU-Net against pyramid scene parsing network, residual U-Net, U-Net++, and traditional U-Net models. Performance was assessed using categorical cross-entropy and dice loss metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Under the dice loss function, RU-Net consistently outperformed competing models across all anatomical planes. It achieved superior scores in accuracy, precision, recall, and intersection over union (IOU), particularly excelling in class-wise IOUs for critical structures such as bladder, rectum, sigmoid colon, and femoral heads.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion:<\/jats:title>\n                    <jats:p>The architecture improved both boundary delineation and overall prediction quality. It is established that the proposed RU-Net shows its supremacy under dice loss conditions in terms of proper prediction and segmentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>RU-Net offers a robust solution for multiorgan segmentation in gynecological BT. Its advanced design effectively addresses anatomical distortions, demonstrating strong potential to improve clinical outcomes in magnetic resonance-guided treatment planning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jmp.jmp_35_26","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T13:01:42Z","timestamp":1790168502000},"page":"578-590","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Multiorgan Segmentation in Magnetic Resonance Images for Gynecological Brachytherapy Using Residuals in Residual Dense Block"],"prefix":"10.4103","volume":"51","author":[{"given":"Suresh","family":"Das","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, Narayana Superspeciality Hospital, Howrah, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prasun","family":"Sanki","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Netaji Subhas Engineering College, Kolkata, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siladittya","family":"Manna","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hongkong Baptist University, Kowloon Tong, Hongkong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Subhayan","family":"Mondal","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Narayana Superspeciality Hospital, Howrah, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saumik","family":"Bhattacharya","sequence":"additional","affiliation":[{"name":"Department of E & ECE, Indian Institute of Technology, Kharagpur, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sayantari","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Department of Physics, National Institute of Technology, Durgapur, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"R1-20260925","first-page":"394","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA Cancer J Clin"},{"key":"R2-20260925","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1001\/jamaoncol.2015.0735","article-title":"The global burden of cancer 2013","volume":"1","author":"Fitzmaurice","year":"2015","journal-title":"JAMA Oncol"},{"key":"R4-20260925","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1186\/s13014-019-1392-z","article-title":"Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer","volume":"14","author":"Ahn","year":"2019","journal-title":"Radiat Oncol"},{"key":"R5-20260925","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s00066-021-01867-1","article-title":"The value of brachytherapy in the age of advanced external beam radiotherapy: A review of the literature in terms of dosimetry","volume":"198","author":"Major","year":"2022","journal-title":"Strahlenther Onkol"},{"key":"R6-20260925","doi-asserted-by":"crossref","first-page":"S112","DOI":"10.1016\/j.brachy.2018.04.206","article-title":"Clinical implementation of automated ICRU 89 reporting for definitive management of cervical cancer with intracavitary brachytherapy","volume":"17","author":"Schroeder","year":"2018","journal-title":"Brachytherapy"},{"key":"R7-20260925","first-page":"103","article-title":"Artificial intelligence for image segmentation in radiation oncology","author":"Feng","year":"2023","journal-title":"Artificial Intelligence in Radiation Oncology. 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