{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:29:24Z","timestamp":1767324564465,"version":"3.48.0"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032128393","type":"print"},{"value":"9783032128409","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\/978-3-032-12840-9_1","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:25:18Z","timestamp":1767324318000},"page":"3-17","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Box it and\u00a0Track it: A Weakly Supervised Framework for\u00a0Cell Tracking"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9274-3757","authenticated-orcid":false,"given":"Nabeel","family":"Khalid","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8780-253X","authenticated-orcid":false,"given":"Mohammadmahdi","family":"Koochali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4785-2588","authenticated-orcid":false,"given":"Khola","family":"Naseem","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5180-9704","authenticated-orcid":false,"given":"Gillian","family":"Lovell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6333-314X","authenticated-orcid":false,"given":"Bianca","family":"Migliori","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1021-2467","authenticated-orcid":false,"given":"Daniel A.","family":"Porto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3799-6094","authenticated-orcid":false,"given":"Johan","family":"Trygg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6100-8255","authenticated-orcid":false,"given":"Andreas","family":"Dengel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4239-6520","authenticated-orcid":false,"given":"Sheraz","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Anjum, S., Gurari, D.: CTMC: cell tracking with mitosis detection dataset challenge. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 982\u2013983 (2020)","DOI":"10.1109\/CVPRW50498.2020.00499"},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. EURASIP J. Image Video Process. (2008)","DOI":"10.1155\/2008\/246309"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: Celltrack r-cnn: a novel end-to-end deep neural network for cell segmentation and tracking in microscopy images. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 779\u2013782. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9434057"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Cheng, B., Parkhi, O., Kirillov, A.: Pointly-supervised instance segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2617\u20132626 (2022)","DOI":"10.1109\/CVPR52688.2022.00264"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Edlund, C., et al.: Livecell-a large-scale dataset for label-free live cell segmentation. Nat. Methods (2021)","DOI":"10.1038\/s41592-021-01249-6"},{"key":"1_CR6","doi-asserted-by":"publisher","unstructured":"Gallusser, B., Weigert, M.: Trackastra: transformer-based cell tracking for live-cell microscopy. In: European Conference on Computer Vision, pp. 467\u2013484. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-73116-7_27","DOI":"10.1007\/978-3-031-73116-7_27"},{"key":"1_CR7","doi-asserted-by":"publisher","unstructured":"Khalid, N., et al.: Bounding box is all you need: learning to segment cells in 2d microscopic images via box annotations. In: Annual Conference on Medical Image Understanding and Analysis, pp. 314\u2013328. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-66955-2_22","DOI":"10.1007\/978-3-031-66955-2_22"},{"key":"1_CR8","doi-asserted-by":"publisher","unstructured":"Khalid, N., Caroprese, M., Lovell, G., Trygg, J., Dengel, A., Ahmed, S.: Cellspot: deep learning-based efficient cell center detection in microscopic images. In: International Conference on Artificial Neural Networks, pp. 215\u2013229. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-72353-7_16","DOI":"10.1007\/978-3-031-72353-7_16"},{"key":"1_CR9","doi-asserted-by":"publisher","unstructured":"Khalid, N., et al.: Pace: point annotation-based cell segmentation for efficient microscopic image analysis. In: International Conference on Artificial Neural Networks. Springer, Heidelberg (2023). https:\/\/doi.org\/10.1007\/978-3-031-44210-0_44","DOI":"10.1007\/978-3-031-44210-0_44"},{"key":"1_CR10","doi-asserted-by":"publisher","unstructured":"Khalid, N., et al.: Sat: segment and track anything for microscopy. In: Proceedings of the 17th International Conference on Agents and Artificial Intelligence, vol. 2: ICAART, pp. 286\u2013297. INSTICC, SciTePress (2025). https:\/\/doi.org\/10.5220\/0013154200003890","DOI":"10.5220\/0013154200003890"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Khalid, N., et al.: Deepmucs: a framework for co-culture microscopic image analysis: from generation to segmentation. In: 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE (2022)","DOI":"10.1109\/BHI56158.2022.9926936"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Khalid, N., et al.: Deepcens: an end-to-end pipeline for cell and nucleus segmentation in microscopic images. In: 2021 International Joint Conference on Neural Networks (IJCNN). IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9533624"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Khalid, N., et al.: Deepcis: an end-to-end pipeline for cell-type aware instance segmentation in microscopic images. In: 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE (2021)","DOI":"10.1109\/BHI50953.2021.9508480"},{"key":"1_CR14","doi-asserted-by":"publisher","unstructured":"Khalid, N., et al.: Point2mask: A weakly supervised approach for cell segmentation using point annotation. In: Medical Image Understanding and Analysis: 26th Annual Conference, MIUA 2022, Cambridge, UK, 27\u201329 July 2022, Proceedings. Springer, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-031-12053-4_11","DOI":"10.1007\/978-3-031-12053-4_11"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et\u00a0al.: Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"1_CR16","unstructured":"Leal-Taix\u00e9, L., Milan, A., Reid, I., Roth, S., Schindler, K.: Motchallenge 2015: towards a benchmark for multi-target tracking. arXiv preprint arXiv:1504.01942 (2015)"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Li, C., Xie, S.S., Wang, J., Sharvia, S., Chan, K.Y.: Sc-track: a robust cell-tracking algorithm for generating accurate single-cell lineages from diverse cell segmentations. Brief. Bioinf. 25(3), bbae192 (2024)","DOI":"10.1093\/bib\/bbae192"},{"issue":"7","key":"1_CR18","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1038\/s41592-023-01879-y","volume":"20","author":"M Ma\u0161ka","year":"2023","unstructured":"Ma\u0161ka, M., et al.: The cell tracking challenge: 10 years of objective benchmarking. Nat. Methods 20(7), 1010\u20131020 (2023)","journal-title":"Nat. Methods"},{"issue":"2","key":"1_CR19","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.tcb.2015.09.003","volume":"26","author":"P Masuzzo","year":"2016","unstructured":"Masuzzo, P., Van Troys, M., Ampe, C., Martens, L.: Taking aim at moving targets in computational cell migration. Trends Cell Biol. 26(2), 88\u2013110 (2016)","journal-title":"Trends Cell Biol."},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"Meijering, E., Dzyubachyk, O., Smal, I., van Cappellen, W.A.: Tracking in cell and developmental biology. In: Seminars in Cell & Developmental Biology, vol.\u00a020, pp. 894\u2013902. Elsevier (2009)","DOI":"10.1016\/j.semcdb.2009.07.004"},{"key":"1_CR21","unstructured":"Moen, E., et\u00a0al.: Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning. In: Biorxiv, p. 803205 (2019)"},{"key":"1_CR22","unstructured":"Ravi, N., et\u00a0al.: Sam 2: segment anything in images and videos. arXiv preprint arXiv:2408.00714 (2024)"},{"key":"1_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/978-3-319-48881-3_2","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"E Ristani","year":"2016","unstructured":"Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for\u00a0multi-target, multi-camera tracking. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 17\u201335. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-48881-3_2"},{"issue":"12","key":"1_CR24","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0243219","volume":"15","author":"T Scherr","year":"2020","unstructured":"Scherr, T., et al.: Cell segmentation and tracking using cnn-based distance predictions and a graph-based matching strategy. PLoS ONE 15(12), e0243219 (2020)","journal-title":"PLoS ONE"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Stringer, C., Wang, T., Michaelos, M., Pachitariu, M.: Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods (2020)","DOI":"10.1101\/2020.02.02.931238"},{"issue":"1","key":"1_CR26","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1038\/s41592-020-01018-x","volume":"18","author":"C Stringer","year":"2021","unstructured":"Stringer, C., et al.: Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18(1), 100\u2013106 (2021)","journal-title":"Nat. Methods"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Wang, X., Chen, H.: Boxinst: high-performance instance segmentation with box annotations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5443\u20135452 (2021)","DOI":"10.1109\/CVPR46437.2021.00540"},{"key":"1_CR28","unstructured":"Ulman, V., et\u00a0al.: An objective comparison of cell-tracking algorithms. Nat. Methods (2017)"},{"key":"1_CR29","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: 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 (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"1_CR30","doi-asserted-by":"publisher","DOI":"10.1017\/S2633903X23000120","volume":"3","author":"R Wang","year":"2023","unstructured":"Wang, R., Butt, D., Cross, S., Verkade, P., Achim, A.: Bright-field to fluorescence microscopy image translation for cell nuclei health quantification. Biol. Imaging 3, e12 (2023)","journal-title":"Biol. Imaging"},{"key":"1_CR31","unstructured":"Yang, C.Y., Huang, H.W., Chai, W., Jiang, Z., Hwang, J.N.: Samurai: adapting segment anything model for zero-shot visual tracking with motion-aware memory. arXiv preprint arXiv:2411.11922 (2024)"},{"key":"1_CR32","doi-asserted-by":"crossref","unstructured":"Yazdi, R., Khotanlou, H.: A survey on automated cell tracking: challenges and solutions. Multimedia Tools Appl. (2024)","DOI":"10.1007\/s11042-024-18697-9"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-12840-9_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:25:21Z","timestamp":1767324321000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-12840-9_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032128393","9783032128409"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-12840-9_1","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":"DAGM GCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"DAGM German Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Freiburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","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":"26 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"47","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dagm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dagm-gcpr.de\/year\/2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}