{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T01:45:49Z","timestamp":1772847949005,"version":"3.50.1"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T00:00:00Z","timestamp":1747872000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T00:00:00Z","timestamp":1747872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100005247","name":"University of British Columbia","doi-asserted-by":"publisher","award":["1"],"award-info":[{"award-number":["1"]}],"id":[{"id":"10.13039\/501100005247","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005247","name":"University of British Columbia","doi-asserted-by":"publisher","award":["1"],"award-info":[{"award-number":["1"]}],"id":[{"id":"10.13039\/501100005247","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"DOI":"10.1007\/s11548-025-03414-0","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T05:45:59Z","timestamp":1747892759000},"page":"1441-1449","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A-MFST: adaptive multi-flow sparse tracker for real-time tissue tracking under occlusion"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9465-3562","authenticated-orcid":false,"given":"Yuxin","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zijian","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam","family":"Schmidt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Septimiu E.","family":"Salcudean","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,22]]},"reference":[{"key":"3414_CR1","unstructured":"Ravi N, Gabeur V, Hu Y-T, Hu R, Ryali C, Ma T, Khedr H, R\u00e4dle R, Rolland C, Gustafson L, et al. (2024) SAM 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714"},{"key":"3414_CR2","doi-asserted-by":"crossref","unstructured":"Neoral M, \u0160er\u1ef3ch J, Matas J (2024) MFT: Long-term tracking of every pixel. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 6837\u20136847","DOI":"10.1109\/WACV57701.2024.00669"},{"key":"3414_CR3","unstructured":"Schmidt A, Mohareri O, DiMaio S, Salcudean SE (2023) STIR: Surgical tattoos in infrared. arXiv preprint arXiv:2309.16782"},{"key":"3414_CR4","first-page":"13610","volume":"35","author":"C Doersch","year":"2022","unstructured":"Doersch C, Gupta A, Markeeva L, Recasens A, Smaira L, Aytar Y, Carreira J, Zisserman A, Yang Y (2022) TAP-Vid: A benchmark for tracking any point in a video. Advances in Neural Information Processing Systems 35:13610\u201313626","journal-title":"Advances in Neural Information Processing Systems"},{"key":"3414_CR5","doi-asserted-by":"crossref","unstructured":"Schmidt A, Mohareri O, DiMaio S, Yip MC, Salcudean SE (2024) Tracking and mapping in medical computer vision: A review. Medical Image Analysis, 103131","DOI":"10.1016\/j.media.2024.103131"},{"key":"3414_CR6","doi-asserted-by":"crossref","unstructured":"Schmidt A, Mohareri O, DiMaio S, Salcudean SE (2023) SENDD: Sparse efficient neural depth and deformation for tissue tracking. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 238\u2013248. Springer","DOI":"10.1007\/978-3-031-43996-4_23"},{"key":"3414_CR7","doi-asserted-by":"crossref","unstructured":"Greff K, Belletti F, Beyer L, Doersch C, Du Y, Duckworth D, Fleet DJ, Gnanapragasam D, Golemo F, Herrmann C, Kipf T, Kundu A, Lagun D, Laradji I, Liu H-TD, Meyer H, Miao Y, Nowrouzezahrai D, Oztireli C, Pot E, Radwan N, Rebain D, Sabour S, Sajjadi MSM, Sela M, Sitzmann V, Stone A, Sun D, Vora S, Wang Z, Wu T, Yi KM, Zhong F, Tagliasacchi A (2022) Kubric: a scalable dataset generator","DOI":"10.1109\/CVPR52688.2022.00373"},{"key":"3414_CR8","doi-asserted-by":"crossref","unstructured":"Grasa OG, Civera J, Montiel J (2011) EKF monocular SLAM with relocalization for laparoscopic sequences. In: 2011 IEEE International Conference on Robotics and Automation, pp. 4816\u20134821. IEEE","DOI":"10.1109\/ICRA.2011.5980059"},{"issue":"1","key":"3414_CR9","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1109\/TMI.2013.2282997","volume":"33","author":"OG Grasa","year":"2013","unstructured":"Grasa OG, Bernal E, Casado S, Gil I, Montiel J (2013) Visual SLAM for handheld monocular endoscope. IEEE Transactions on Medical Imaging 33(1):135\u2013146","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"4","key":"3414_CR10","doi-asserted-by":"publisher","first-page":"4068","DOI":"10.1109\/LRA.2018.2856519","volume":"3","author":"J Song","year":"2018","unstructured":"Song J, Wang J, Zhao L, Huang S, Dissanayake G (2018) MIS-SLAM: Real-time large-scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing. IEEE Robotics and Automation Letters 3(4):4068\u20134075","journal-title":"IEEE Robotics and Automation Letters"},{"issue":"8","key":"3414_CR11","doi-asserted-by":"publisher","first-page":"4055","DOI":"10.1109\/TIP.2017.2712279","volume":"26","author":"C Zhang","year":"2017","unstructured":"Zhang C, Chen Z, Wang M, Li M, Jiang S (2017) Robust non-local tv-$$l^{1}$$ optical flow estimation with occlusion detection. IEEE Transactions on Image Processing 26(8):4055\u20134067","journal-title":"IEEE Transactions on Image Processing"},{"key":"3414_CR12","doi-asserted-by":"crossref","unstructured":"Teed Z, Deng J (2020) RAFT: Recurrent all-pairs field transforms for optical flow. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part II 16, pp. 402\u2013419. Springer","DOI":"10.1007\/978-3-030-58536-5_24"},{"key":"3414_CR13","doi-asserted-by":"crossref","unstructured":"Huang Z, Shi X, Zhang C, Wang Q, Cheung KC, Qin H, Dai J, Li H (2022) FlowFormer: A transformer architecture for optical flow. In: European Conference on Computer Vision, pp. 668\u2013685. Springer","DOI":"10.1007\/978-3-031-19790-1_40"},{"key":"3414_CR14","doi-asserted-by":"crossref","unstructured":"Guo J, Wang J, Li Z, Jia T, Dou Q, Liu Y-H (2024) Ada-Tracker: Soft tissue tracking via inter-frame and adaptive-template matching. arXiv preprint arXiv:2403.06479","DOI":"10.1109\/ICRA57147.2024.10611030"},{"key":"3414_CR15","doi-asserted-by":"crossref","unstructured":"Zheng Y, Harley AW, Shen B, Wetzstein G, Guibas LJ (2023)PointOdyssey: A large-scale synthetic dataset for long-term point tracking. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 19855\u201319865","DOI":"10.1109\/ICCV51070.2023.01818"},{"key":"3414_CR16","doi-asserted-by":"crossref","unstructured":"Chen W, Schmidt A, Prisman E, Salcudean SE (2024)PIPsUS: Self-supervised dense point tracking in ultrasound. arXiv preprint arXiv:2403.04969","DOI":"10.1007\/978-3-031-73647-6_5"},{"key":"3414_CR17","doi-asserted-by":"crossref","unstructured":"Karaev N, Rocco I, Graham B, Neverova N, Vedaldi A, Rupprecht C (2023)CoTracker: It is better to track together. arXiv preprint arXiv:2307.07635","DOI":"10.1007\/978-3-031-73033-7_2"},{"key":"3414_CR18","doi-asserted-by":"crossref","unstructured":"Xiao Y, Wang Q, Zhang S, Xue N, Peng S, Shen Y, Zhou X (2024)SpatialTracker: Tracking any 2d pixels in 3d space. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20406\u201320417","DOI":"10.1109\/CVPR52733.2024.01929"},{"key":"3414_CR19","doi-asserted-by":"crossref","unstructured":"Wu Z, Schmidt A, Kazanzides P, Salcudean SE (2024)Real-time surgical instrument segmentation in video using point tracking and segment anything. arXiv preprint arXiv:2403.08003","DOI":"10.22541\/au.173089470.05750142\/v1"},{"key":"3414_CR20","doi-asserted-by":"crossref","unstructured":"Mannor S, Jin X, Han J, Jin X, Han J, Jin X, Han J, Zhang X (2011) K-means clustering. Encyclopedia of Machine Learning, 563\u2013564","DOI":"10.1007\/978-0-387-30164-8_425"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-025-03414-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-025-03414-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-025-03414-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T14:41:50Z","timestamp":1751553710000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-025-03414-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,22]]},"references-count":20,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["3414"],"URL":"https:\/\/doi.org\/10.1007\/s11548-025-03414-0","relation":{},"ISSN":["1861-6429"],"issn-type":[{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,22]]},"assertion":[{"value":"10 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 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":"One of the authors, Adam Schmidt, is affiliated with Intuitive Surgical and received support from the company during the development of SENDD, a fundamental algorithm used in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}