{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T22:54:44Z","timestamp":1781564084134,"version":"3.54.5"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T00:00:00Z","timestamp":1777334400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"vor","delay-in-days":49,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100015322","name":"National Cancer Center Japan","doi-asserted-by":"publisher","award":["2021-A-9"],"award-info":[{"award-number":["2021-A-9"]}],"id":[{"id":"10.13039\/100015322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["JP256f0137008"],"award-info":[{"award-number":["JP256f0137008"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Surg"],"DOI":"10.1186\/s12893-026-03787-2","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T13:26:46Z","timestamp":1777382806000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimal quantitative metrics reflecting surgeons\u2019 qualitative evaluation in deep learning-based intraoperative anatomy recognition task: a retrospective study"],"prefix":"10.1186","volume":"26","author":[{"given":"Masahiro","family":"Fuse","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daichi","family":"Kitaguchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Norihito","family":"Kosugi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuto","family":"Ishikawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiro","family":"Hasegawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nobuyoshi","family":"Takeshita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusuke","family":"Kinugasa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masaaki","family":"Ito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,28]]},"reference":[{"key":"3787_CR1","doi-asserted-by":"publisher","first-page":"6105","DOI":"10.1007\/s00464-022-09384-7","volume":"36","author":"D Kitaguchi","year":"2022","unstructured":"Kitaguchi D, Takeshita N, Matsuzaki H, Igaki T, Hasegawa H, Kojima S, et al. Real-time vascular anatomical image navigation for laparoscopic surgery: experimental study. Surg Endosc. 2022;36:6105\u201312.","journal-title":"Surg Endosc"},{"key":"3787_CR2","doi-asserted-by":"crossref","unstructured":"Madad Zadeh S, Francois T, Calvet L, Chauvet P, Canis M, Bartoli A et al. SurgAI: deep learning for computerized laparoscopic image understanding in gynaecology. Surg Endosc. 2020;34:5377\u201383.","DOI":"10.1007\/s00464-019-07330-8"},{"key":"3787_CR3","doi-asserted-by":"publisher","first-page":"1355","DOI":"10.1093\/bjs\/znad249","volume":"110","author":"D Kitaguchi","year":"2023","unstructured":"Kitaguchi D, Harai Y, Kosugi N, Hayashi K, Kojima S, Ishikawa Y, et al. Artificial intelligence for the recognition of key anatomical structures in laparoscopic colorectal surgery. Br J Surg. 2023;110:1355\u20138.","journal-title":"Br J Surg"},{"key":"3787_CR4","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1186\/s12916-019-1426-2","volume":"17","author":"CJ Kelly","year":"2019","unstructured":"Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:195.","journal-title":"BMC Med"},{"key":"3787_CR5","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1001\/jama.2019.10306","volume":"322","author":"NH Shah","year":"2019","unstructured":"Shah NH, Milstein A, Bagley PhD SC. Making machine learning models clinically useful. JAMA. 2019;322:1351\u20132.","journal-title":"JAMA"},{"key":"3787_CR6","first-page":"015006","volume":"5","author":"V Yeghiazaryan","year":"2018","unstructured":"Yeghiazaryan V, Voiculescu I. Family of boundary overlap metrics for the evaluation of medical image segmentation. J Med Imaging (Bellingham). 2018;5:015006.","journal-title":"J Med Imaging (Bellingham)"},{"key":"3787_CR7","doi-asserted-by":"publisher","first-page":"11763","DOI":"10.3934\/mbe.2023523","volume":"20","author":"W Gu","year":"2023","unstructured":"Gu W, Wang H, Liu X, Yin Y, Xu B. Urban scene segmentation model based on multi-scale shuffle features. Math Biosci Eng. 2023;20:11763\u201384.","journal-title":"Math Biosci Eng"},{"key":"3787_CR8","doi-asserted-by":"publisher","unstructured":"Kofler F, Ezhov I, Isensee F, Balsiger F. Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient. 2021. https:\/\/doi.org\/10.48550\/arXiv.2103.06205.","DOI":"10.48550\/arXiv.2103.06205"},{"key":"3787_CR9","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1097\/SLE.0000000000000335","volume":"26","author":"TAT Marcelissen","year":"2016","unstructured":"Marcelissen TAT, Hollander D, Tuytten PP, Tom RAH, Sosef MN. Incidence of iatrogenic ureteral injury during open and laparoscopic colorectal surgery: A single-center experience and review of the literature. Surg Laparosc Endosc Percutan Tech. 2016;26:513\u20135.","journal-title":"Surg Laparosc Endosc Percutan Tech"},{"key":"3787_CR10","doi-asserted-by":"publisher","unstructured":"Zhao H, Shi J, Qi X, Wang X, Jiaya J. Pyramid scene parsing network. 2017. https:\/\/doi.org\/10.48550\/arXiv.1612.01105.","DOI":"10.48550\/arXiv.1612.01105"},{"key":"3787_CR11","doi-asserted-by":"publisher","unstructured":"Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H. Encoder-decoder with atrous separable convolution for semantic image segmentation. 2018. https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"3787_CR12","doi-asserted-by":"publisher","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014. https:\/\/doi.org\/10.48550\/arXiv.1409.1556.","DOI":"10.48550\/arXiv.1409.1556"},{"key":"3787_CR13","doi-asserted-by":"publisher","unstructured":"Chollet F. Xception: deep learning with depthwise separable convolutions. 2017. https:\/\/doi.org\/10.48550\/arXiv.1610.02357.","DOI":"10.48550\/arXiv.1610.02357"},{"key":"3787_CR14","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"TY Lin","year":"2020","unstructured":"Lin TY, Goyal P, Girshick R, He K, Dollar P. Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell. 2020;42:318\u201327.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3787_CR15","doi-asserted-by":"publisher","first-page":"101851","DOI":"10.1016\/j.media.2020.101851","volume":"67","author":"H Kervadec","year":"2021","unstructured":"Kervadec H, Bouchtiba J, Desrosiers C, Granger E, Dolz J, Ben Ayed I. Boundary loss for highly unbalanced segmentation. arXiv:1812.07032. Med Image Anal. 2021;67:101851.","journal-title":"Med Image Anal"},{"key":"3787_CR16","doi-asserted-by":"publisher","unstructured":"Salehi SSM, Erdogmus D, Gholipour A. Tversky loss function for image segmentation using 3d fully convolutional deep networks. International Workshop on Machine Learning in Medical Imaging. 2017. https:\/\/doi.org\/10.48550\/arXiv.1706.05721.","DOI":"10.48550\/arXiv.1706.05721"},{"key":"3787_CR17","doi-asserted-by":"publisher","unstructured":"Abraham N, Khan NM. A novel focal Tversky loss function with improved attention u-net for lesion segmentation. 2018. https:\/\/doi.org\/10.48550\/arXiv.1810.07842.","DOI":"10.48550\/arXiv.1810.07842"},{"key":"3787_CR18","doi-asserted-by":"publisher","unstructured":"Liu L, Jiang H, He P, Chen W, Liu X, Gao J, Han J. On the variance of the adaptive learning rate and beyond. 2021. https:\/\/doi.org\/10.48550\/arXiv.1908.03265.","DOI":"10.48550\/arXiv.1908.03265"},{"key":"3787_CR19","doi-asserted-by":"publisher","unstructured":"Nikolov S, Blackwell S, Zverovitch A, Mendes R, Livne M, De Fauw J et al. Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy. 2018. https:\/\/doi.org\/10.48550\/arXiv.1809.04430.","DOI":"10.48550\/arXiv.1809.04430"},{"key":"3787_CR20","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1109\/34.232073","volume":"15","author":"DP Huttenlocher","year":"1993","unstructured":"Huttenlocher DP, Klanderman GA, Rucklidge WJ. Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell. 1993;15:850\u201363.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3787_CR21","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.radonc.2021.05.003","volume":"160","author":"MV Sherer","year":"2021","unstructured":"Sherer MV, Lin D, Elguindi S, Duke S, Tan LT, Cacicedo J, et al. Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: a critical review. Radiother Oncol. 2021;160:185\u201391.","journal-title":"Radiother Oncol"},{"key":"3787_CR22","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26:297\u2013302.","journal-title":"Ecology"},{"key":"3787_CR23","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s11548-007-0125-1","volume":"2","author":"A Popovic","year":"2007","unstructured":"Popovic A, De la Fuente M, Engelhardt M, Radermacher K. Statistical validation metric for accuracy assessment in medical image segmentation. Int J Comput Assist Rad Surg. 2007;2:169\u201381.","journal-title":"Int J Comput Assist Rad Surg"},{"key":"3787_CR24","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.1002\/sim.1723","volume":"23","author":"KH Zou","year":"2004","unstructured":"Zou KH, Wells WM, Kikinis R, Warfield SK. Three validation metrics for automated probabilistic image segmentation of brain tumours. Stat Med. 2004;23:1259\u201382.","journal-title":"Stat Med"},{"key":"3787_CR25","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s12880-015-0068-x","volume":"15","author":"AA Taha","year":"2015","unstructured":"Taha AA, Hanbury A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging. 2015;15:29.","journal-title":"BMC Med Imaging"},{"key":"3787_CR26","doi-asserted-by":"publisher","unstructured":"Shamir RR, Duchin Y, Kim J, Sapiro G, Harel N. Continuous dice coefficient: a method for evaluating probabilistic segmentations. 2019. https:\/\/doi.org\/10.48550\/arXiv.1906.11031.","DOI":"10.48550\/arXiv.1906.11031"},{"key":"3787_CR27","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1097\/JS9.0000000000000317","volume":"109","author":"S Kojima","year":"2023","unstructured":"Kojima S, Kitaguchi D, Igaki T, Nakajima K, Ishikawa Y, Harai Y, et al. Deep-learning-based semantic segmentation of autonomic nerves from laparoscopic images of colorectal surgery: an experimental pilot study. Int J Surg. 2023;109:813\u201320.","journal-title":"Int J Surg"},{"key":"3787_CR28","doi-asserted-by":"publisher","first-page":"e1596","DOI":"10.1097\/DCR.0000000000003335","volume":"67","author":"S Narihiro","year":"2024","unstructured":"Narihiro S, Kitaguchi D, Hasegawa H, Takeshita N, Ito M. Deep learning-based real-time ureter identification in laparoscopic colorectal surgery. Dis Colon Rectum. 2024;67:e1596\u20139.","journal-title":"Dis Colon Rectum"},{"key":"3787_CR29","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/s41592-023-02151-z","volume":"21","author":"L Maier-Hein","year":"2024","unstructured":"Maier-Hein L, Reinke A, Godau P, Tizabi MD, Buettner F, Christodoulou E, et al. Metrics reloaded: recommendations for image analysis validation. Nat Methods. 2024;21:195\u2013212.","journal-title":"Nat Methods"}],"container-title":["BMC Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12893-026-03787-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12893-026-03787-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12893-026-03787-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T22:26:30Z","timestamp":1781562390000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12893-026-03787-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,28]]},"references-count":29,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["3787"],"URL":"https:\/\/doi.org\/10.1186\/s12893-026-03787-2","relation":{},"ISSN":["1471-2482"],"issn-type":[{"value":"1471-2482","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,28]]},"assertion":[{"value":"28 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The protocol for this research project has been approved by a suitably constituted Ethics Committee of the institution, and it conforms to the provisions of the Declaration of Helsinki. Committee of the National Cancer Center Hospital East, Approval No. 2020\u2009\u2212\u2009269 and 2020\u2009\u2212\u2009315. Informed consent was obtained from the participants.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"416"}}