{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T20:31:56Z","timestamp":1786998716090,"version":"build-2736575974"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"GRRC program of Gyeonggi province","award":["GRRC-Gachon2023(B01)"],"award-info":[{"award-number":["GRRC-Gachon2023(B01)"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-025-01725-x","type":"journal-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T16:05:20Z","timestamp":1761321920000},"page":"3713-3721","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Based Multi-Organ Segmentation in Gynecologic Laparoscopy: Comparative Evaluation of Deep Learning Architectures for Anatomical Precision and Surgical Applicability"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4927-4243","authenticated-orcid":false,"given":"Taehoon","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8163-1424","authenticated-orcid":false,"given":"So Hyeon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0443-0051","authenticated-orcid":false,"given":"Young Jae","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jongwook","family":"Whangbo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9714-6038","authenticated-orcid":false,"given":"Kwang Gi","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,24]]},"reference":[{"key":"1725_CR1","doi-asserted-by":"publisher","unstructured":"Restaino S, Graziano A, Ronsini C, Paparcura F, Bagolin M, Cinti MC, Castenetto A, Titone F, Rensi M, Driul L, Vizzielli G: A case report of uterine leiomyosarcoma: unusual clinical presentation with unilateral hydronephrosis and importance of an appropriate diagnosis. Minerva Obstet Gynecol 76(2):200\u2013204, 2024. https:\/\/doi.org\/10.23736\/S2724-606X.22.05131-4","DOI":"10.23736\/S2724-606X.22.05131-4"},{"issue":"5","key":"1725_CR2","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1097\/SLA.0000000000004351","volume":"275","author":"P Mascagni","year":"2022","unstructured":"Mascagni P, Vardazaryan A, Alapatt D, Urade T, Emre T, Fiorillo C, Pessaux P, Mutter D, Marescaux J, Costamagna G, Dallemagne B, Padoy N: Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Ann Surg 275(5):955\u2013961, 2022. https:\/\/doi.org\/10.1097\/SLA.0000000000004351","journal-title":"Ann Surg"},{"key":"1725_CR3","doi-asserted-by":"crossref","unstructured":"Srikanth, R., Kanya, N., & Kumar, P.S.R. (2024). Detection of normal and abnormal brain tumor MRI images using machine learning approaches. Journal of Autonomous Intelligence, 199\u2013204. Available at: http:\/\/jai.frontci.com\/index.php\/jai\/article\/view\/1092.","DOI":"10.32629\/jai.v7i4.1092"},{"key":"1725_CR4","doi-asserted-by":"crossref","unstructured":"Hayat, M., Aramvith, S., & Achakulvisut, T. (2024). SEGSRNet for Stereo-Endoscopic Image Super-Resolution and Surgical Instrument Segmentation. arXiv:2404.13330. Retrieved from https:\/\/arxiv.org\/abs\/2404.13330.","DOI":"10.1109\/EMBC53108.2024.10782794"},{"issue":"5","key":"1725_CR5","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/TMI.2016.2553401","volume":"35","author":"H Greenspan","year":"2016","unstructured":"Greenspan, H., van Ginneken, B., & Summers, R. M. (2016). Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. IEEE Transactions on Medical Imaging, 35(5), 1153\u20131159. https:\/\/doi.org\/10.1109\/TMI.2016.2553401.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"1725_CR6","doi-asserted-by":"publisher","unstructured":"Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & van Ginneken, B. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60\u201388. https:\/\/doi.org\/10.1016\/j.media.2017.07.005.","DOI":"10.1016\/j.media.2017.07.005"},{"key":"1725_CR7","unstructured":"Hernandez, B. (2024). Deep Learning for Medical Image Segmentation in MRI Scans: Implements deep learning techniques for accurate segmentation of structures in MRI scans. Journal of Deep Learning in Genomic Data Analysis. https:\/\/thelifescience.org\/index.php\/jdlgda\/article\/view\/17"},{"key":"1725_CR8","doi-asserted-by":"publisher","unstructured":"Mohammed, M. S., Zrigui, S., & Zrigui, M. (2023). Teeth Disease Recognition Based on X-ray Images. In Computational Collective Intelligence (pp. 651\u2013659). Springer. https:\/\/doi.org\/10.1007\/978-3-031-41456-5_52.","DOI":"10.1007\/978-3-031-41456-5_52"},{"key":"1725_CR9","doi-asserted-by":"publisher","unstructured":"Bourdel, Nicolas, et al. \"Use of augmented reality in gynecologic surgery to visualize adenomyomas.\"\u00a0Journal of minimally invasive gynecology\u00a026.6 (2019): 1177\u20131180. https:\/\/doi.org\/10.1016\/j.jmig.2019.04.003","DOI":"10.1016\/j.jmig.2019.04.003"},{"key":"1725_CR10","unstructured":"Gardiner, B., Zhang, H., & Fayemiwo, M. (2024). Enhancing Medical Image Segmentation using Hexagonal Convolutional Neural Networks. Artificial Intelligence in Medicine. Retrieved from https:\/\/pure.ulster.ac.uk\/files\/207602605\/AIME_Manuscript_BGardiner.pdf."},{"key":"1725_CR11","doi-asserted-by":"publisher","first-page":"5626","DOI":"10.1038\/s41467-020-19449-7","volume":"11","author":"O Schoppe","year":"2020","unstructured":"Schoppe O, Pan C, Coronel J, Mai H, Rong Z, Todorov MI, Menze BH: Deep learning-enabled multi-organ segmentation in whole-body mouse scans. Nat Commun 11:5626, 2020","journal-title":"Nat Commun"},{"key":"1725_CR12","doi-asserted-by":"crossref","unstructured":"Kurata, Yasuhisa, et al. \"Automatic segmentation of the uterus on MRI using a convolutional neural network.\"\u00a0Computers in biology and medicine\u00a0114 (2019): 103438.","DOI":"10.1016\/j.compbiomed.2019.103438"},{"key":"1725_CR13","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.ejmp.2021.05.003","volume":"85","author":"Y Fu","year":"2021","unstructured":"Fu, Y., Lei, Y., Wang, T., Curran, W.J., Liu, T., Yang, X. (2021). A review of deep learning based methods for medical image multi-organ segmentation. Physics and Medicine, 85, 107\u2013122. https:\/\/doi.org\/10.1016\/j.ejmp.2021.05.003","journal-title":"Physics and Medicine"},{"key":"1725_CR14","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/TMI.2016.2593957","volume":"36","author":"AP Twinanda","year":"2016","unstructured":"Twinanda AP, Shehata S, Mutter D, Marescaux J, de Mathelin M, Padoy N: EndoNet: a deep architecture for recognition tasks on laparoscopic videos. IEEE Trans Med Imaging 36:86\u201397, 2016.","journal-title":"IEEE Trans Med Imaging"},{"issue":"5","key":"1725_CR15","doi-asserted-by":"publisher","first-page":"464","DOI":"10.3760\/cma.j.cn441530-20240125-00041","volume":"27","author":"KC Zhang","year":"2024","unstructured":"Zhang KC, Qiao Z, Yang L, Zhang T, Liu FL, Sun DC, Xie TY, Guo L, Lu CR: Computer-vision-based artificial intelligence for detection and recognition of instruments and organs during radical laparoscopic gastrectomy for gastric cancer: a multicenter study. Zhonghua Wei Chang Wai Ke Za Zhi 27(5):464\u2013470, 2024. https:\/\/doi.org\/10.3760\/cma.j.cn441530-20240125-00041.","journal-title":"Zhonghua Wei Chang Wai Ke Za Zhi"},{"key":"1725_CR16","doi-asserted-by":"crossref","unstructured":"Kurata, Yasuhisa, et al. \"Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network.\" Scientific Reports 11.1 (2021): 14440.","DOI":"10.1038\/s41598-021-93792-7"},{"key":"1725_CR17","doi-asserted-by":"crossref","unstructured":"Liu, ZhiWei, et al. \"3D segmentation of uterine fibroids based on deep supervision and an attention gate.\"\u00a0Frontiers in Oncology\u00a015 (2025): 1522399.","DOI":"10.3389\/fonc.2025.1522399"},{"key":"1725_CR18","unstructured":"Bradski, G., & Kaehler, A. (2000). OpenCV. Dr. Dobb\u2019s Journal of Software Tools, 3(2)."},{"key":"1725_CR19","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1725_CR20","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhang, G., Tang, J., & Luo, B. (2024). MFADU-Net: Multi-Level Feature Fusion and Atrous Decoder Enhancements in DoubleU-Net for Medical Image Segmentation. Research Square. https:\/\/www.researchsquare.com\/article\/rs-4665126\/latest.","DOI":"10.21203\/rs.3.rs-4665126\/v1"},{"key":"1725_CR21","doi-asserted-by":"publisher","unstructured":"Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P. SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers. Adv Neural Inf Process Syst. 2021;34:12077\u201312090. https:\/\/doi.org\/10.48550\/arXiv.2105.15203","DOI":"10.48550\/arXiv.2105.15203"},{"key":"1725_CR22","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., Glocker, B., & Rueckert, D. (2018). Attention U-Net: Learning Where to Look for the Pancreas. arXiv preprint arXiv:1804.03999."},{"key":"1725_CR23","first-page":"17819","volume":"7","author":"P Jiang","year":"2019","unstructured":"Jiang, P., Zhang, G., Tang, J., & Luo, B. (2019). MANet: A Multi-Level Attention Network for Background Subtraction. IEEE Access, 7, 17819\u201317830.","journal-title":"IEEE Access"},{"key":"1725_CR24","unstructured":"EfficientNet: Rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th International Conference on Machine Learning. PMLR; 2019:6105\u20136114."}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01725-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-025-01725-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-025-01725-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T19:32:20Z","timestamp":1786995140000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-025-01725-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,24]]},"references-count":24,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["1725"],"URL":"https:\/\/doi.org\/10.1007\/s10278-025-01725-x","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,24]]},"assertion":[{"value":"3 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This study was conducted with the approval of the Gachon University Gil Medical Center Institutional Review Board (IRB No. GDIRB2020-346).","order":1,"name":"Ethics","label":"Ethical Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The requirement for informed consent was waived by the IRB due to the retrospective nature of the study.","order":2,"name":"Ethics","label":"Informed Consent","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest.","order":3,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}