{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T11:36:57Z","timestamp":1767008217234,"version":"3.37.3"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T00:00:00Z","timestamp":1656288000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T00:00:00Z","timestamp":1656288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/http:\/\/dx.doi.org\/10.13039\/100000009","name":"fOUNDATION fOR tHE nATIONAL iNSTITUTES oF hEALTH","doi-asserted-by":"publisher","award":["1r01dK12344501a1"],"award-info":[{"award-number":["1r01dK12344501a1"]}],"id":[{"id":"10.13039\/http:\/\/dx.doi.org\/10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/http:\/\/dx.doi.org\/10.13039\/100006521","name":"sCHOOL oF mEDICINE, sTANFORD uNIVERSITY","doi-asserted-by":"publisher","award":["162769"],"award-info":[{"award-number":["162769"]}],"id":[{"id":"10.13039\/http:\/\/dx.doi.org\/10.13039\/100006521","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s11548-022-02691-3","type":"journal-article","created":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T11:02:41Z","timestamp":1656327761000},"page":"1497-1505","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Open surgery tool classification and hand utilization using a multi-camera system"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2299-9383","authenticated-orcid":false,"given":"Kristina","family":"Basiev","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam","family":"Goldbraikh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carla M.","family":"Pugh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shlomi","family":"Laufer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"2691_CR1","doi-asserted-by":"crossref","unstructured":"Zisimopoulos O, Flouty E, Luengo I, Giataganas P, Nehme J, Chow A, Stoyanov D (2018) Deepphase: surgical phase recognition in cataracts videos. In: international conference on medical image computing and computer-assisted intervention. Springer, pp 265\u2013272","DOI":"10.1007\/978-3-030-00937-3_31"},{"key":"2691_CR2","doi-asserted-by":"crossref","unstructured":"Primus MJ, Putzgruber-Adamitsch D, Taschwer M, M\u00fcnzer B, El-Shabrawi Y, B\u00f6sz\u00f6rmenyi L, Schoeffmann K (2018) Frame-based classification of operation phases in cataract surgery videos. In: International Conference on Multimedia Modeling. Springer, pp 241\u2013253","DOI":"10.1007\/978-3-319-73603-7_20"},{"issue":"1","key":"2691_CR3","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1177\/1553350607299777","volume":"14","author":"Y Xiao","year":"2007","unstructured":"Xiao Y, Schimpff S, Mackenzie C, Merrell R, Entin E, Voigt R, Jarrell B (2007) Video technology to advance safety in the operating room and perioperative environment. Surg Innov 14(1):52\u201361","journal-title":"Surg Innov"},{"key":"2691_CR4","doi-asserted-by":"crossref","unstructured":"Jin A, Yeung S, Jopling J, Krause J, Azagury D, Milstein A, Fei-Fei L (2018) Tool detection and operative skill assessment in surgical videos using region-based convolutional neural networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, pp 691\u2013699","DOI":"10.1109\/WACV.2018.00081"},{"issue":"8","key":"2691_CR5","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1089\/lap.2014.0015","volume":"24","author":"RW Partridge","year":"2014","unstructured":"Partridge RW, Hughes MA, Brennan PM, Hennessey IA (2014) Accessible laparoscopic instrument tracking (\u201cinstrac\u2019\u2019): construct validity in a take-home box simulator. J Laparoendosc Adv Surg Tech 24(8):578\u2013583","journal-title":"J Laparoendosc Adv Surg Tech"},{"key":"2691_CR6","doi-asserted-by":"crossref","unstructured":"Liu T, Meng Q, Vlontzos A, Tan J, Rueckert D, Kainz B (2020) Ultrasound video summarization using deep reinforcement learning. In: international conference on medical image computing and computer-assisted intervention. Springer, pp 483\u2013492","DOI":"10.1007\/978-3-030-59716-0_46"},{"key":"2691_CR7","doi-asserted-by":"crossref","unstructured":"Sznitman R, Basu A, Richa R, Handa J, Gehlbach P, Taylor RH, Jedynak B, Hager GD (2011) Unified detection and tracking in retinal microsurgery. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp 1\u20138","DOI":"10.1007\/978-3-642-23623-5_1"},{"key":"2691_CR8","doi-asserted-by":"crossref","unstructured":"Richa R, Balicki M, Meisner E, Sznitman R, Taylor R, Hager G (2011) Visual tracking of surgical tools for proximity detection in retinal surgery. In: international conference on information processing in computer-assisted interventions. Springer, pp 55\u201366","DOI":"10.1007\/978-3-642-21504-9_6"},{"key":"2691_CR9","doi-asserted-by":"crossref","unstructured":"Goldbraikh A, D\u2019Angelo A-L, Pugh CM, Laufer S (2022) Video-based fully automatic assessment of open surgery suturing skills. Int J Comput Assist Radiol Surg, 1\u201312","DOI":"10.1007\/s11548-022-02559-6"},{"issue":"2","key":"2691_CR10","doi-asserted-by":"publisher","first-page":"15","DOI":"10.3390\/jimaging7020015","volume":"7","author":"T Shimizu","year":"2021","unstructured":"Shimizu T, Hachiuma R, Kajita H, Takatsume Y, Saito H (2021) Hand motion-aware surgical tool localization and classification from an egocentric camera. J Imaging 7(2):15","journal-title":"J Imaging"},{"key":"2691_CR11","doi-asserted-by":"publisher","first-page":"78193","DOI":"10.1109\/ACCESS.2020.2989807","volume":"8","author":"Y Liu","year":"2020","unstructured":"Liu Y, Zhao Z, Chang F, Hu S (2020) An anchor-free convolutional neural network for real-time surgical tool detection in robot-assisted surgery. IEEE Access 8:78193\u201378201","journal-title":"IEEE Access"},{"issue":"3","key":"2691_CR12","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1080\/21681163.2020.1835550","volume":"9","author":"S Kondo","year":"2021","unstructured":"Kondo S (2021) Lapformer: surgical tool detection in laparoscopic surgical video using transformer architecture. Comput Methods Biomech Biomed Eng Imaging Vis 9(3):302\u2013307","journal-title":"Comput Methods Biomech Biomed Eng Imaging Vis"},{"issue":"8","key":"2691_CR13","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"J Schmidhuber","year":"1997","unstructured":"Schmidhuber J, Hochreiter S et al (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"2691_CR14","unstructured":"Jocher G Yolov5. Accessed on 04 Nov 2021. https:\/\/github.com\/ultralytics\/yolov5"},{"key":"2691_CR15","unstructured":"Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767"},{"key":"2691_CR16","first-page":"91","volume":"28","author":"S Ren","year":"2015","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28:91\u201399","journal-title":"Adv Neural Inf Process Syst"},{"key":"2691_CR17","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.media.2018.05.001","volume":"47","author":"H Al Hajj","year":"2018","unstructured":"Al Hajj H, Lamard M, Conze P-H, Cochener B, Quellec G (2018) Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks. Med Image Anal 47:203\u2013218","journal-title":"Med Image Anal"},{"key":"2691_CR18","unstructured":"Zhang M, Cheng X, Copeland D, Desai A, Guan MY, Brat GA, Yeung S (2020) Using computer vision to automate hand detection and tracking of surgeon movements in videos of open surgery. In: AMIA Annual Symposium Proceedings. American Medical Informatics Association, vol 2020, p 1373"},{"issue":"1","key":"2691_CR19","doi-asserted-by":"publisher","first-page":"0245230","DOI":"10.1371\/journal.pone.0245230","volume":"16","author":"M Seeland","year":"2021","unstructured":"Seeland M, M\u00e4der P (2021) Multi-view classification with convolutional neural networks. Plos One 16(1):0245230","journal-title":"Plos One"},{"key":"2691_CR20","doi-asserted-by":"publisher","unstructured":"Silva B, Barbosa-Anda FR, Batista J (2021) Multi-view fine-grained vehicle classification with multi-loss learning. In: 2021 IEEE international conference on autonomous robot systems and competitions (ICARSC), pp 209\u2013214. https:\/\/doi.org\/10.1109\/ICARSC52212.2021.9429780","DOI":"10.1109\/ICARSC52212.2021.9429780"},{"issue":"16","key":"2691_CR21","doi-asserted-by":"publisher","first-page":"5311","DOI":"10.3390\/s21165311","volume":"21","author":"P Jakob","year":"2021","unstructured":"Jakob P, Madan M, Schmid-Schirling T, Valada A (2021) Multi-perspective anomaly detection. Sensors 21(16):5311","journal-title":"Sensors"},{"issue":"1","key":"2691_CR22","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1007\/s11263-009-0273-6","volume":"87","author":"L Sigal","year":"2010","unstructured":"Sigal L, Balan AO, Black MJ (2010) Humaneva: synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. Int J Comput Vis 87(1):4\u201327","journal-title":"Int J Comput Vis"},{"issue":"7","key":"2691_CR23","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1109\/TPAMI.2013.248","volume":"36","author":"C Ionescu","year":"2013","unstructured":"Ionescu C, Papava D, Olaru V, Sminchisescu C (2013) Human3. 6m: large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE Trans Pattern Anal Mach Intell 36(7):1325\u20131339","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"10","key":"2691_CR24","doi-asserted-by":"publisher","first-page":"2684","DOI":"10.1109\/TPAMI.2019.2916873","volume":"42","author":"J Liu","year":"2019","unstructured":"Liu J, Shahroudy A, Perez M, Wang G, Duan L-Y, Kot AC (2019) Ntu rgb+ d 120: a large-scale benchmark for 3d human activity understanding. IEEE Trans Pattern Anal Mach Intell 42(10):2684\u20132701","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2691_CR25","doi-asserted-by":"crossref","unstructured":"Sigurdsson GA, Gupta A, Schmid C, Farhadi A, Alahari K (2018) Actor and observer: Joint modeling of first and third-person videos. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp 7396\u20137404","DOI":"10.1109\/CVPR.2018.00772"},{"key":"2691_CR26","doi-asserted-by":"crossref","unstructured":"Li W, Wong Y, Liu A-A, Li Y, Su Y-T, Kankanhalli M (2017) Multi-camera action dataset for cross-camera action recognition benchmarking. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE. pp 187\u2013196","DOI":"10.1109\/WACV.2017.28"},{"issue":"1","key":"2691_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-020-01120-2","volume":"32","author":"A Kadkhodamohammadi","year":"2021","unstructured":"Kadkhodamohammadi A, Padoy N (2021) A generalizable approach for multi-view 3d human pose regression. Mach Vis Appl 32(1):1\u201314","journal-title":"Mach Vis Appl"},{"key":"2691_CR28","doi-asserted-by":"crossref","unstructured":"Schmidt A, Sharghi A, Haugerud H, Oh D, Mohareri O (2021) Multi-view surgical video action detection via mixed global view attention. In: international conference on medical image computing and computer-assisted intervention. Springer, pp 626\u2013635","DOI":"10.1007\/978-3-030-87202-1_60"},{"issue":"1","key":"2691_CR29","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1097\/SLA.0000000000002863","volume":"271","author":"JJ Jung","year":"2020","unstructured":"Jung JJ, J\u00fcni P, Lebovic G, Grantcharov T (2020) First-year analysis of the operating room black box study. Ann Surg 271(1):122\u2013127","journal-title":"Ann Surg"},{"key":"2691_CR30","doi-asserted-by":"crossref","unstructured":"Ayas S, Gordon L, Donmez B, Grantcharov T (2021) The effect of intraoperative distractions on severe technical events in laparoscopic bariatric surgery. Surg Endosc 35(8):4569\u20134580","DOI":"10.1007\/s00464-020-07878-w"},{"key":"2691_CR31","doi-asserted-by":"crossref","unstructured":"Kajita, H., Takatsume, Y., Shimizu, T., Saito, H., Kishi, K.: Overhead multiview camera system for recording open surgery. Plastic and Reconstructive Surgery Global Open 8(4) (2020)","DOI":"10.1097\/GOX.0000000000002765"},{"key":"2691_CR32","doi-asserted-by":"crossref","unstructured":"Hachiuma R, Shimizu T, Saito H, Kajita H, Takatsume Y (2020) Deep selection: a fully supervised camera selection network for surgery recordings. In: international conference on medical image computing and computer-assisted intervention. Springer, pp 419\u2013428","DOI":"10.1007\/978-3-030-59716-0_40"},{"key":"2691_CR33","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/j.jss.2021.07.003","volume":"268","author":"H Mohamadipanah","year":"2021","unstructured":"Mohamadipanah H, Kearse L, Witt A, Wise B, Yang S, Goll C, Pugh C (2021) Can deep learning algorithms help identify surgical workflow and techniques? J Surg Res 268:318\u2013325","journal-title":"J Surg Res"},{"key":"2691_CR34","unstructured":"Angeles-Ceron JC, Ochoa-Ruiz G, Chang L, Ali S (2021) Real-time instance segmentation of surgical instruments using attention and multi-scale feature fusion. arXiv preprint arXiv:2111.04911"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-022-02691-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-022-02691-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-022-02691-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T03:41:34Z","timestamp":1658547694000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-022-02691-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,27]]},"references-count":34,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["2691"],"URL":"https:\/\/doi.org\/10.1007\/s11548-022-02691-3","relation":{},"ISSN":["1861-6429"],"issn-type":[{"type":"electronic","value":"1861-6429"}],"subject":[],"published":{"date-parts":[[2022,6,27]]},"assertion":[{"value":"12 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2022","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"}},{"value":"Study approval was granted by the University of Wisconsin Health Sciences Institutional Review Board, and written informed consent was obtained from all participants.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}