{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:46:49Z","timestamp":1792082809784,"version":"4.4.0"},"reference-count":42,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"2","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:sec>\n                    <jats:title>Background and Purpose:<\/jats:title>\n                    <jats:p>Precise delineation of pelvic organs-at-risk (OARs) is crucial for high-dose-rate brachytherapy (HDR-BT) in cervical cancer treatment. While deep learning methods have shown promise in automatic delineation, substantial and complex organ deformations pose significant challenges. This study presents a novel approach to address these issues.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>\n                      We introduce a coarse-to-refine strategy for annotation, utilizing limited existing data to expedite the process. Combined with deformation-based data augmentation, we incorporate this information into a three-dimensional attention U-Net (C2FAU-Net). The study included 100 cervical cancer patients, with OARs annotated by experienced oncologists. The dataset was divided into 80 patients for training, 10 for validation, and 10 for testing. To assess the delineation performance, we employed the volumetric dice similarity coefficient (DSC), 95\n                      <jats:sup>th<\/jats:sup>\n                      percentile Hausdorff Distance (HD95), average symmetric surface distance (ASSD), precision, and recall. We compared the time consumed by manual delineation versus artificial intelligence (AI)-assisted delineation. Dosimetric parameters were compared using different contours to evaluate the clinical impact of the automated approach.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Our method achieved an average DSC of 89.7%, HD95 of 3.61 mm, and an ASSD of 1.02 mm in the test cohort. The AI-assisted method significantly reduced the manual delineation time from 17.85 \u00b1 3.84 min to 7.54 \u00b1 4.95 min. No significant difference was observed in \u0394D\n                      <jats:sub>2cc<\/jats:sub>\n                      , \u0394D\n                      <jats:sub>1cc<\/jats:sub>\n                      , \u0394D\n                      <jats:sub>0.1cc<\/jats:sub>\n                      , and \u0394D\n                      <jats:sub>max<\/jats:sub>\n                      for bladder, rectum, and sigmoid when comparing contours generated by C2FAU-Net to those created manually.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>We introduce an effective automatic delineation framework for pelvic OARs, enhancing efficiency within the HDR-BT workflow and potentially improving treatment outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jmp.jmp_65_25","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T04:01:13Z","timestamp":1751515273000},"page":"217-226","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":1,"title":["C2FAU-Net: A Deep Learning Approach with Multi-scale Strategy for Automated Delineation of Organs-at-risk in Cervical Cancer High-dose Rate Brachytherapy"],"prefix":"10.4103","volume":"50","author":[{"given":"Man","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenfeng","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Dai","sequence":"additional","affiliation":[{"name":"Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaoqin","family":"Xie","sequence":"additional","affiliation":[{"name":"Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijian","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nanjie","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongjie","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shupeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaokun","family":"Liang","sequence":"additional","affiliation":[{"name":"Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenbin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2025,7,3]]},"reference":[{"key":"R1-20250703","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/S0140-6736(18)32470-X","article-title":"Cervical cancer","volume":"393","author":"Cohen","year":"2019","journal-title":"Lancet"},{"key":"R2-20250703","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.brachy.2018.11.009","article-title":"Brachytherapy: A critical component of primary radiation therapy for cervical cancer: From the society of gynecologic oncology (SGO) and the American Brachytherapy Society (ABS)","volume":"18","author":"Holschneider","year":"2019","journal-title":"Brachytherapy"},{"key":"R3-20250703","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.radonc.2020.04.017","article-title":"Fast and fully-automated multi-criterial treatment planning for adaptive HDR brachytherapy for locally advanced cervical cancer","volume":"148","author":"Oud","year":"2020","journal-title":"Radiother Oncol"},{"key":"R4-20250703","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.radonc.2018.02.014","article-title":"Fast and robust adaptation of organs-at-risk delineations from planning scans to match daily anatomy in pre-treatment scans for online-adaptive radiotherapy of abdominal tumors","volume":"127","author":"Gupta","year":"2018","journal-title":"Radiother Oncol"},{"key":"R5-20250703","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.radonc.2019.09.022","article-title":"Improving automatic delineation for head and neck organs at risk by deep learning contouring","volume":"142","author":"van Dijk","year":"2020","journal-title":"Radiother Oncol"},{"key":"R6-20250703","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.radonc.2007.11.030","article-title":"Evaluation of an atlas-based automatic segmentation software for the delineation of brain organs at risk in a radiation therapy clinical context","volume":"87","author":"Isambert","year":"2008","journal-title":"Radiother Oncol"},{"key":"R7-20250703","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.radonc.2015.05.012","article-title":"Multi-subject atlas-based auto-segmentation reduces interobserver variation and improves dosimetric parameter consistency for organs at risk in nasopharyngeal carcinoma: A multi-institution clinical study","volume":"115","author":"Tao","year":"2015","journal-title":"Radiother Oncol"},{"key":"R8-20250703","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.ijrobp.2023.04.026","article-title":"Patient-specific auto-segmentation on daily kVCT images for adaptive radiation therapy","volume":"117","author":"Chen","year":"2023","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"R9-20250703","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.radonc.2022.10.029","article-title":"Comparison of atlas-based and deep learning methods for organs at risk delineation on head-and-neck CT images using an automated treatment planning system","volume":"177","author":"Costea","year":"2022","journal-title":"Radiother Oncol"},{"key":"R10-20250703","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1016\/j.ijrobp.2023.05.034","article-title":"Comprehensive evaluation of a deep learning model for automatic organs-at-risk segmentation on heterogeneous computed tomography images for abdominal radiation therapy","volume":"117","author":"Liao","year":"2023","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"R11-20250703","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.compbiomed.2019.04.018","article-title":"Deep learning for identifying radiogenomic associations in breast cancer","volume":"109","author":"Zhu","year":"2019","journal-title":"Comput Biol Med"},{"key":"R12-20250703","doi-asserted-by":"crossref","first-page":"1712","DOI":"10.1002\/mp.15490","article-title":"Automatic segmentation of high-risk clinical target volume for tandem-and-ovoids brachytherapy patients using an asymmetric dual-path convolutional neural network","volume":"49","author":"Cao","year":"2022","journal-title":"Med Phys"},{"key":"R13-20250703","doi-asserted-by":"crossref","first-page":"e13988","DOI":"10.1002\/acm2.13988","article-title":"Self-configuring nnU-Net for automatic delineation of the organs at risk and target in high-dose rate cervical brachytherapy, a low\/middle-income country\u2019s experience","volume":"24","author":"Duprez","year":"2023","journal-title":"J Appl Clin Med Phys"},{"key":"R14-20250703","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1186\/s13014-020-01562-y","article-title":"Atlas-based auto-segmentation for postoperative radiotherapy planning in endometrial and cervical cancers","volume":"15","author":"Kim","year":"2020","journal-title":"Radiat Oncol"},{"key":"R15-20250703","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1186\/s13014-022-02121-3","article-title":"A deep learning-based self-adapting ensemble method for segmentation in gynecological brachytherapy","volume":"17","author":"Li","year":"2022","journal-title":"Radiat Oncol"},{"key":"R16-20250703","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.radonc.2021.03.030","article-title":"Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer","volume":"159","author":"Mohammadi","year":"2021","journal-title":"Radiother Oncol"},{"key":"R17-20250703","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1148\/rg.2018170055","article-title":"Translabial US and dynamic MR imaging of the pelvic floor: Normal anatomy and dysfunction","volume":"38","author":"Chami\u00e9","year":"2018","journal-title":"Radiographics"},{"key":"R18-20250703","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.radonc.2019.08.023","article-title":"Image registration, contour propagation and dose accumulation of external beam and brachytherapy in gynecological radiotherapy","volume":"143","author":"Swamidas","year":"2020","journal-title":"Radiother Oncol"},{"key":"R19-20250703","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1186\/s13014-022-02088-1","article-title":"Pelvic U-Net: Multi-label semantic segmentation of pelvic organs at risk for radiation therapy anal cancer patients using a deeply supervised shuffle attention convolutional neural network","volume":"17","author":"Lempart","year":"2022","journal-title":"Radiat Oncol"},{"key":"R20-20250703","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1093\/jamia\/ocy068","article-title":"Opportunities and challenges in developing deep learning models using electronic health records data: A systematic review","volume":"25","author":"Xiao","year":"2018","journal-title":"J Am Med Inform Assoc"},{"key":"R21-20250703","doi-asserted-by":"crossref","DOI":"10.1109\/TMI.2019.2897538","article-title":"VoxelMorph: A Learning Framework for Deformable Medical Image Registration","author":"Balakrishnan","year":"2019","journal-title":"IEEE Trans Med Imaging"},{"key":"R22-20250703","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1109\/83.855432","article-title":"Sequential karhunen-loeve basis extraction and its application to images","volume":"9","author":"Levey","year":"2000","journal-title":"IEEE Trans Image Process"},{"key":"R23-20250703","doi-asserted-by":"crossref","first-page":"102984","DOI":"10.1016\/j.media.2023.102984","article-title":"A statistical deformation model-based data augmentation method for volumetric medical image segmentation","volume":"91","author":"He","year":"2024","journal-title":"Med Image Anal"},{"key":"R24-20250703","doi-asserted-by":"crossref","first-page":"7998","DOI":"10.1007\/s00330-022-08784-6","article-title":"Radiology artificial intelligence: A systematic review and evaluation of methods (RAISE)","volume":"32","author":"Kelly","year":"2022","journal-title":"Eur Radiol"},{"key":"R25-20250703","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1038\/s42256-019-0099-z","article-title":"Clinically applicable deep learning framework for organs at risk delineation in CT images","volume":"1","author":"Tang","year":"2019","journal-title":"Nat Mach Intell"},{"key":"R26-20250703","doi-asserted-by":"crossref","first-page":"e230024","DOI":"10.1148\/ryai.230024","article-title":"TotalSegmentator: Robust segmentation of 104 anatomic structures in CT images","volume":"5","author":"Wasserthal","year":"2023","journal-title":"Radiol Artif Intell"},{"key":"R28-20250703","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","article-title":"Attention gated networks: Learning to leverage salient regions in medical images","volume":"53","author":"Schlemper","year":"2019","journal-title":"Med Image Anal"},{"key":"R29-20250703","doi-asserted-by":"crossref","first-page":"102078","DOI":"10.1016\/j.artmed.2021.102078","article-title":"Transfer learning in medical image segmentation: New insights from analysis of the dynamics of model parameters and learned representations","volume":"116","author":"Karimi","year":"2021","journal-title":"Artif Intell Med"},{"key":"R30-20250703","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1002\/acm2.13024","article-title":"Automatic segmentation and applicator reconstruction for CT-based brachytherapy of cervical cancer using 3D convolutional neural networks","volume":"21","author":"Zhang","year":"2020","journal-title":"J Appl Clin Med Phys"},{"key":"R31-20250703","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1016\/j.ijrobp.2021.06.014","article-title":"Single Application multifractionated image guided adaptive high-dose-rate brachytherapy for cervical cancer: Dosimetric and clinical outcomes","volume":"111","author":"Mahantshetty","year":"2021","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"R32-20250703","first-page":"1","article-title":"A review of the application of deep learning in brachytherapy","volume":"7","author":"Hu","year":"2020","journal-title":"OALib"},{"key":"R33-20250703","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.inffus.2021.10.007","article-title":"Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence","volume":"79","author":"Holzinger","year":"2021","journal-title":"Inf Fusion"},{"key":"R34-20250703","doi-asserted-by":"crossref","first-page":"E33","DOI":"10.1038\/s41586-023-06559-7","article-title":"Publisher correction: Scientific discovery in the age of artificial intelligence","volume":"621","author":"Wang","year":"2023","journal-title":"Nature"},{"key":"R35-20250703","doi-asserted-by":"crossref","first-page":"115406","DOI":"10.1016\/j.eswa.2021.115406","article-title":"TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach","volume":"183","author":"Aghamohammadi","year":"2021","journal-title":"Expert Syst Appl"},{"key":"R36-20250703","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1002\/mp.14072","article-title":"A convolutional neural network-based model observer for breast CT images","volume":"47","author":"Kim","year":"2020","journal-title":"Med Phys"},{"key":"R37-20250703","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s00701-024-05973-8","article-title":"NnU-Net versus mesh growing algorithm as a tool for the robust and timely segmentation of neurosurgical 3D images in contrast-enhanced T1 MRI scans","volume":"166","author":"de Boer","year":"2024","journal-title":"Acta Neurochir (Wien)"},{"key":"R38-20250703","doi-asserted-by":"crossref","first-page":"181","DOI":"10.3390\/bioengineering10020181","article-title":"Comparing 3D, 2.5D, and 2D approaches to brain image auto-segmentation","volume":"10","author":"Avesta","year":"2023","journal-title":"Bioengineering (Basel)"},{"key":"R39-20250703","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.radonc.2019.10.019","article-title":"Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning","volume":"144","author":"Wong","year":"2020","journal-title":"Radiother Oncol"},{"key":"R40-20250703","doi-asserted-by":"crossref","first-page":"15","DOI":"10.15212\/RADSCI-2023-0018","article-title":"Large scale models in radiology: revolutionizing the future of medical imaging","volume":"3","author":"Li","year":"2024","journal-title":"Radiology Sci"},{"key":"R41-20250703","doi-asserted-by":"crossref","first-page":"100047","DOI":"10.1016\/j.metrad.2023.100047","article-title":"Review of large vision models and visual prompt engineering","volume":"1","author":"Wang","year":"2023","journal-title":"Meta-Radiology"},{"key":"R42-20250703","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.acra.2019.10.006","article-title":"Exploring large-scale public medical image datasets","volume":"27","author":"Oakden-Rayner","year":"2020","journal-title":"Acad Radiol"},{"key":"R43-20250703","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1002\/mp.16965","article-title":"Technical note: Generalizable and promptable artificial intelligence model to augment clinical delineation in radiation oncology","volume":"51","author":"Zhang","year":"2024","journal-title":"Med Phys"}],"container-title":["Journal of Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/jmp.jmp_65_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T18:57:38Z","timestamp":1786647458000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/jmp.jmp_65_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":42,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.4103\/jmp.jmp_65_25","relation":{},"ISSN":["0971-6203","1998-3913"],"issn-type":[{"value":"0971-6203","type":"print"},{"value":"1998-3913","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4]]},"assertion":[{"value":"2025-03-11","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-09","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}