{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T23:35:52Z","timestamp":1767310552599,"version":"3.48.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032097835","type":"print"},{"value":"9783032097842","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-09784-2_10","type":"book-chapter","created":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T23:33:04Z","timestamp":1767310384000},"page":"95-104","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["When Tracking Fails: Analyzing Failure Modes of\u00a0SAM2 for\u00a0Point-Based Tracking in\u00a0Surgical Videos"],"prefix":"10.1007","author":[{"given":"Woowon","family":"Jang","sequence":"first","affiliation":[]},{"given":"Jiwon","family":"Im","sequence":"additional","affiliation":[]},{"given":"Juseung","family":"Choi","sequence":"additional","affiliation":[]},{"given":"Niki","family":"Rashidian","sequence":"additional","affiliation":[]},{"given":"Wesley","family":"De Neve","sequence":"additional","affiliation":[]},{"given":"Utku","family":"Ozbulak","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"issue":"6","key":"10_CR1","doi-asserted-by":"publisher","first-page":"4423","DOI":"10.1007\/s00500-020-05453-y","volume":"25","author":"M Bansal","year":"2021","unstructured":"Bansal, M., Kumar, M., Kumar, M., Kumar, K.: An efficient technique for object recognition using Shi-Tomasi corner detection algorithm. Soft. Comput. 25(6), 4423\u20134432 (2021)","journal-title":"Soft. Comput."},{"key":"10_CR2","unstructured":"Chen, X.: Real-time semantic segmentation algorithms for enhanced augmented reality. J. Comput. Innov. 3(1) (2023)"},{"key":"10_CR3","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"issue":"1","key":"10_CR4","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/s10462-022-10176-7","volume":"56","author":"M Gao","year":"2023","unstructured":"Gao, M., Zheng, F., Yu, J.J., Shan, C., Ding, G., Han, J.: Deep learning for video object segmentation: a review. Artif. Intell. Rev. 56(1), 457\u2013531 (2023)","journal-title":"Artif. Intell. Rev."},{"key":"10_CR5","unstructured":"Geetha, A.S., Hussain, M.: From SAM to SAM 2: exploring improvements in meta\u2019s segment anything model (2024). https:\/\/arxiv.org\/abs\/2408.06305"},{"key":"10_CR6","unstructured":"Hong, W.Y., Kao, C.L., Kuo, Y.H., Wang, J.R., Chang, W.L., Shih, C.S.: Cholecseg8k: a semantic segmentation dataset for laparoscopic cholecystectomy based on cholec80. arXiv preprint arXiv:2012.12453 (2020)"},{"key":"10_CR7","unstructured":"Jiaxing, Z., Hao, T.: SAM2 for image and video segmentation: a comprehensive survey (2025). https:\/\/arxiv.org\/abs\/2503.12781"},{"issue":"1","key":"10_CR8","first-page":"42","volume":"1","author":"NK Kaur","year":"2014","unstructured":"Kaur, N.K., Kaur, U., Singh, D.: K-medoid clustering algorithm - a review. Int. J. Comput. Appl. Technol. 1(1), 42\u201345 (2014)","journal-title":"Int. J. Comput. Appl. Technol."},{"key":"10_CR9","doi-asserted-by":"publisher","unstructured":"Kitaguchi, D., Takeshita, N., Hasegawa, H., Ito, M.: Artificial intelligence-based computer vision in surgery: recent advances and future perspectives. Ann. Gastroenterol. Surg. 6(1), 29\u201336 (2022). https:\/\/doi.org\/10.1002\/ags3.12513, https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1002\/ags3.12513","DOI":"10.1002\/ags3.12513"},{"issue":"9","key":"10_CR10","doi-asserted-by":"publisher","first-page":"940","DOI":"10.3390\/bioengineering11090940","volume":"11","author":"M Kulyabin","year":"2024","unstructured":"Kulyabin, M., et al.: Segment anything in optical coherence tomography: SAM 2 for volumetric segmentation of retinal biomarkers. Bioengineering 11(9), 940 (2024)","journal-title":"Bioengineering"},{"key":"10_CR11","unstructured":"Liu, H., Zhang, E., Wu, J., Hong, M., Jin, Y.: Surgical SAM 2: real-time segment anything in surgical video by efficient frame pruning. arXiv preprint arXiv:2408.07931 (2024)"},{"key":"10_CR12","doi-asserted-by":"publisher","unstructured":"Loftus, T.J., et al.: Artificial intelligence and surgical decision-making. JAMA Surg. 155(2), 148\u2013158 (2020). https:\/\/doi.org\/10.1001\/jamasurg.2019.4917","DOI":"10.1001\/jamasurg.2019.4917"},{"key":"10_CR13","unstructured":"Ma, J., et al.: MedSAM2: segment anything in 3D medical images and videos (2025). https:\/\/arxiv.org\/abs\/2504.03600"},{"key":"10_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/978-3-031-73360-4_2","volume-title":"Computational Mathematics Modeling in Cancer Analysis - CMMCA 2024","author":"SA Mousavi","year":"2024","unstructured":"Mousavi, S.A., et al.: A reference-based approach for tumor size estimation in monocular laparoscopic videos. In: Wu, J., Qin, W., Li, C., Kim, B. (eds.) CMMCA 2024. LNCS, vol. 15181, pp. 11\u201320. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-73360-4_2"},{"key":"10_CR15","doi-asserted-by":"publisher","unstructured":"Obuchowicz, R., Strzelecki, M., Pi\u00f3rkowski, A.: Clinical applications of artificial intelligence in medical imaging and image processing\u2014a review. Cancers 16(10) (2024). https:\/\/doi.org\/10.3390\/cancers16101870, https:\/\/www.mdpi.com\/2072-6694\/16\/10\/1870","DOI":"10.3390\/cancers16101870"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Ozbulak, U., et al.: Revisiting the evaluation bias introduced by frame sampling strategies in surgical video segmentation using SAM2 (2025). https:\/\/arxiv.org\/abs\/2502.20934","DOI":"10.1007\/978-3-032-05870-6_20"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Raji\u010d, F., Ke, L., Tai, Y.W., Tang, C.K., Danelljan, M., Yu, F.: Segment anything meets point tracking. In: 2025 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 9302\u20139311. IEEE (2025)","DOI":"10.1109\/WACV61041.2025.00901"},{"key":"10_CR18","unstructured":"Ravi, N., et\u00a0al.: SAM 2: segment anything in images and videos. arXiv preprint arXiv:2408.00714 (2024)"},{"key":"10_CR19","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"}],"container-title":["Lecture Notes in Computer Science","Collaborative Intelligence and Autonomy in Image-Guided Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-09784-2_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T23:33:06Z","timestamp":1767310386000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-09784-2_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032097835","9783032097842"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-09784-2_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"COLAS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Collaborative Intelligence and Autonomy in Image-Guided Surgery","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"colas2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/sites.google.com\/view\/miccai-2025-colas\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}