{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T00:06:18Z","timestamp":1759017978515,"version":"3.44.0"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T00:00:00Z","timestamp":1751500800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T00:00:00Z","timestamp":1751500800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100000646","name":"Japan Society for the Promotion of Science London","doi-asserted-by":"publisher","award":["21K19898","26108006","17H00867"],"award-info":[{"award-number":["21K19898","26108006","17H00867"]}],"id":[{"id":"10.13039\/501100000646","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020963","name":"Moonshot Research and Development Program","doi-asserted-by":"publisher","award":["JPMJMS2033"],"award-info":[{"award-number":["JPMJMS2033"]}],"id":[{"id":"10.13039\/501100020963","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001695","name":"Japan Science and Technology Corporation","doi-asserted-by":"publisher","award":["JPMJCR20D5"],"award-info":[{"award-number":["JPMJCR20D5"]}],"id":[{"id":"10.13039\/501100001695","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"DOI":"10.1007\/s11548-025-03352-x","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T08:13:09Z","timestamp":1751530389000},"page":"1899-1910","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing YOLO for laparoscopic tool detection: novel data augmentation and structural modifications addressing mis-detection of bifurcated targets"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8760-1103","authenticated-orcid":false,"given":"Yuzhang","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuichiro","family":"Hayashi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7714-422X","authenticated-orcid":false,"given":"Masahiro","family":"Oda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0100-4797","authenticated-orcid":false,"given":"Kensaku","family":"Mori","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,3]]},"reference":[{"key":"3352_CR1","doi-asserted-by":"publisher","DOI":"10.3389\/fsurg.2021.799442","volume":"8","author":"I Alkatout","year":"2021","unstructured":"Alkatout I, Mechler U, Mettler L, Pape J, Maass N, Biebl M, Gitas G, Lagan\u00e0 AS, Freytag D (2021) The development of laparoscopy: a historical overview. Front Surg 8:799442","journal-title":"Front Surg"},{"key":"3352_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101994","volume":"70","author":"K Hasan","year":"2021","unstructured":"Hasan K, Calvet L, Rabbani N, Adrien B (2021) Detection, segmentation, and 3D pose estimation of surgical tools using convolutional neural networks and algebraic geometry. Med Image Anal 70:101994","journal-title":"Med Image Anal"},{"key":"3352_CR3","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1007\/s11548-019-01958-6","volume":"14","author":"NC Innocent","year":"2019","unstructured":"Innocent NC, Didier M, Jacques M, Nicolas P (2019) Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos. Int J Comput Assist Radiol Surg 14:1059\u20131067","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"3352_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102433","volume":"78","author":"NC Innocent","year":"2022","unstructured":"Innocent NC, Tong Y, Cristians G, Barbara S, Pietro M, Didier M, Jacques M, Nicolas P (2022) Rendezvous: attention mechanisms for the recognition of surgical action triplets in endoscopic videos. Med Image Anal 78:102433","journal-title":"Med Image Anal"},{"issue":"1","key":"3352_CR5","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1080\/24699322.2020.1801842","volume":"25","author":"Y Congmin","year":"2020","unstructured":"Congmin Y, Zijian Z, Sanyuan H (2020) Image-based laparoscopic tool detection and tracking using convolutional neural networks: a review of the literature. Comput Assist Surg 25(1):15\u201328","journal-title":"Comput Assist Surg"},{"key":"3352_CR6","unstructured":"Jocher G (2020) Ultralytics YOLOv5. https:\/\/github.com\/ultralytics\/yolov5. Accessed 11 Jan 2024"},{"key":"3352_CR7","unstructured":"Chuyi L, Lulu L, Hongliang J, Kaiheng W, Yifei G, Liang L, Zaidan K, Qingyuan L, Meng C, Weiqiang N, et\u00a0al.: (2022) YOLOv6: a single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976"},{"key":"3352_CR8","doi-asserted-by":"crossref","unstructured":"Wang C-Y, Bochkovskiy A, Liao H-YM (2023) YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7464\u20137475","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"3352_CR9","unstructured":"Jocher G, Chaurasia A, Qiu J (2023) Ultralytics YOLOv8. https:\/\/github.com\/ultralytics\/ultralytics. Accessed 11 Jan 2024"},{"key":"3352_CR10","doi-asserted-by":"publisher","DOI":"10.1049\/htl2.12072","author":"Y Liu","year":"2024","unstructured":"Liu Y, Hayashi Y, Oda M, Kitasaka T, Mori K (2024) YOLOv7-RepFPN: improving real-time performance of laparoscopic tool detection on embedded systems. Healthcare Technol Lett. https:\/\/doi.org\/10.1049\/htl2.12072","journal-title":"Healthcare Technol Lett"},{"key":"3352_CR11","first-page":"679","volume":"25","author":"N Babak","year":"2022","unstructured":"Babak N, Ganesh S, Venkat D (2022) A contextual detector of surgical tools in laparoscopic videos using deep learning. Surg Endosc 25:679\u2013688","journal-title":"Surg Endosc"},{"issue":"3","key":"3352_CR12","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/s11548-022-02559-6","volume":"17","author":"A Goldbraikh","year":"2022","unstructured":"Goldbraikh A, D\u2019Angelo AL, Pugh CM, Laufer S (2022) Video-based fully automatic assessment of open surgery suturing skills. Int J Comput Assist Radiol Surg 17(3):437\u2013448","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"3352_CR13","unstructured":"Max A, Alex S, Thomas K, Zichen Z, Rahul D, Yun-Hsuan S, Nicola R, Iro L, Niveditha K, Sebastian B, et al (2017) Robotic instrument segmentation challenge. arXiv preprint arXiv:1902.06426"},{"key":"3352_CR14","unstructured":"Max A, Satoshi K, Sebastian B, Stefan L, Rahim K, Imanol L, Felix F, Evangello F, Ahmed M, Marius P, et\u00a0al (2020) 2018 robotic scene segmentation challenge. arXiv preprint arXiv:2001.11190"},{"issue":"16","key":"3352_CR15","doi-asserted-by":"publisher","first-page":"7190","DOI":"10.3390\/s23167190","volume":"23","author":"W Gang","year":"2023","unstructured":"Gang W, Yanfei C, Pei A, Hanyu H, Jinghu H, Tiange H (2023) UAV-YOLOv8: a small-object-detection model based on improved YOLOv8 for UAV aerial photography scenarios. Sensors 23(16):7190","journal-title":"Sensors"},{"key":"3352_CR16","unstructured":"Zhang H, Cisse M, Dauphin YN, Lopez-Paz D (2018) Mixup: beyond empirical risk minimization. In: International conference on learning representations"},{"key":"3352_CR17","doi-asserted-by":"crossref","unstructured":"Raja S, Tie L (2022) No more strided convolutions or pooling: a new CNN building block for low-resolution images and small objects. In: Joint European conference on machine learning and knowledge discovery in databases, pp 443\u2013459. Springer","DOI":"10.1007\/978-3-031-26409-2_27"},{"key":"3352_CR18","unstructured":"Zhi Z, Tong H, Hang Z, Zhongyue Z, Junyuan X, Mu L (2019) Bag of freebies for training object detection neural networks. arXiv preprint arXiv:1902.04103"},{"issue":"4","key":"3352_CR19","doi-asserted-by":"publisher","first-page":"1680","DOI":"10.3390\/make5040083","volume":"5","author":"J Terven","year":"2023","unstructured":"Terven J, C\u00f3rdova-Esparza D-M, Romero-Gonz\u00e1lez J-A (2023) A comprehensive review of YOLO architectures in computer vision: from YOLOv1 to YOLOv8 and YOLO-NAS. Mach Learn Knowl Extract 5(4):1680\u20131716","journal-title":"Mach Learn Knowl Extract"},{"key":"3352_CR20","doi-asserted-by":"publisher","first-page":"228853","DOI":"10.1109\/ACCESS.2020.3046258","volume":"8","author":"P Shi","year":"2020","unstructured":"Shi P, Zhao Z, Hu S, Chang F (2020) Real-time surgical tool detection in minimally invasive surgery based on attention-guided convolutional neural network. IEEE Access 8:228853\u2013228862","journal-title":"IEEE Access"},{"key":"3352_CR21","unstructured":"Devries T, Taylor GW (2017) Improved regularization of convolutional neural networks with Cutout. arXiv preprint arXiv:1708.04552"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-025-03352-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-025-03352-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-025-03352-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T11:22:13Z","timestamp":1758972133000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-025-03352-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,3]]},"references-count":21,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["3352"],"URL":"https:\/\/doi.org\/10.1007\/s11548-025-03352-x","relation":{},"ISSN":["1861-6429"],"issn-type":[{"type":"electronic","value":"1861-6429"}],"subject":[],"published":{"date-parts":[[2025,7,3]]},"assertion":[{"value":"19 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2025","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 authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest:"}}]}}