{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T16:03:07Z","timestamp":1790179387271,"version":"4.1.0"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032381712","type":"print"},{"value":"9783032381729","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"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":[[2027]]},"DOI":"10.1007\/978-3-032-38172-9_25","type":"book-chapter","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T15:17:08Z","timestamp":1790176628000},"page":"259-269","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploiting Longitudinal Context in\u00a0Clinician-Verified Interactive Lesion Tracking"],"prefix":"10.1007","author":[{"given":"Yannick","family":"Kirchhoff","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximilian","family":"Rokuss","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel Philipp","family":"Mertens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"F\u00fcller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Hamm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Schreyer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"Ritter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus","family":"Maier-Hein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"25_CR1","doi-asserted-by":"crossref","unstructured":"Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R.L., Soerjomataram, I., Jemal, A.: Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 74(3), 229\u2013263 (2024)","DOI":"10.3322\/caac.21834"},{"key":"25_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2025.103525","volume":"102","author":"M de Grauw","year":"2025","unstructured":"de Grauw, M.: The uls23 challenge: a baseline model and benchmark dataset for 3d universal lesion segmentation in computed tomography. Med. Image Anal. 102, 103525 (2025). https:\/\/doi.org\/10.1016\/j.media.2025.103525","journal-title":"Med. Image Anal."},{"key":"25_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/978-3-030-72084-1_11","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"S Denner","year":"2021","unstructured":"Denner, S., et al.: Spatio-temporal learning from longitudinal data for multiple sclerosis lesion segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2020. LNCS, vol. 12658, pp. 111\u2013121. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72084-1_11"},{"key":"25_CR4","doi-asserted-by":"publisher","first-page":"110746","DOI":"10.52202\/079017-3516","volume":"37","author":"Y Du","year":"2024","unstructured":"Du, Y., Bai, F., Huang, T., Zhao, B.: Segvol: universal and interactive volumetric medical image segmentation. Adv. Neural. Inf. Process. Syst. 37, 110746\u2013110783 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"25_CR5","doi-asserted-by":"crossref","unstructured":"Eisenhauer, E.A., et al.: New response evaluation criteria in solid tumours: revised recist guideline (version 1.1). Eur. J. Cancer 45(2), 228\u2013247 (2009)","DOI":"10.1016\/j.ejca.2008.10.026"},{"key":"25_CR6","unstructured":"Hering, A., et al.: Whole-body soft-tissue lesion tracking and segmentation in longitudinal ct imaging studies. In: Medical Imaging with Deep Learning, pp. 312\u2013326. PMLR (2021)"},{"issue":"9","key":"25_CR7","doi-asserted-by":"publisher","first-page":"1689","DOI":"10.1007\/s11548-024-03181-4","volume":"19","author":"A Hering","year":"2024","unstructured":"Hering, A., et al.: Improving assessment of lesions in longitudinal ct scans: a bi-institutional reader study on an ai-assisted registration and volumetric segmentation workflow. Int. J. Comput. Assist. Radiol. Surg. 19(9), 1689\u20131697 (2024)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"25_CR8","unstructured":"Isensee, F., et al.: nninteractive: redefining 3d promptable segmentation (2025). https:\/\/arxiv.org\/abs\/2503.08373"},{"key":"25_CR9","doi-asserted-by":"publisher","unstructured":"Isensee, F., et al.: nnU-Net revisited: a call for rigorous validation in 3D medical image segmentation . In: Linguraru, M.G., et al. (ed.) MICCAI 2024. LNCS, vol. 15009. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72114-4_47","DOI":"10.1007\/978-3-031-72114-4_47"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Jacobs, C., et\u00a0al.: Assisted versus manual interpretation of low-dose ct scans for lung cancer screening: impact on lung-rads agreement. Radiol. Imaging Cancer 3(5), e200160 (2021)","DOI":"10.1148\/rycan.2021200160"},{"key":"25_CR11","doi-asserted-by":"publisher","unstructured":"Kovacs, B., et al.: Anatomy-informed data augmentation for enhanced prostate cancer detection. In: Greenspan, H., et al. (ed.) MICCAI 2023. LNCS, vol. 14226. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43990-2_50","DOI":"10.1007\/978-3-031-43990-2_50"},{"key":"25_CR12","doi-asserted-by":"publisher","unstructured":"K\u00fcstner, T., Peisen, F., Gatidis, S., Wagner, A., Megne, O., Othman, A., Sanner, A., Lo\u00df\u00a0au, T., Moltz, J.H., Kohlbrandt, T., Hering, A.: Longitudinal-ct. https:\/\/fdat.uni-tuebingen.de\/records\/qwsry-7t837 (Mar 2025). https:\/\/doi.org\/10.57754\/FDAT.qwsry-7t837, version v1, Published March 16, 2025","DOI":"10.57754\/FDAT.qwsry-7t837"},{"issue":"9","key":"25_CR13","doi-asserted-by":"publisher","first-page":"1560","DOI":"10.1093\/neuonc\/noab071","volume":"23","author":"SL Lu","year":"2021","unstructured":"Lu, S.L., et al.: Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks. Neuro Oncol. 23(9), 1560\u20131568 (2021)","journal-title":"Neuro Oncol."},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Rocholl, N., Smit, E., Prokop, M., Hering, A.: Unstable prompts, unreliable segmentations: a challenge for longitudinal lesion analysis, arXiv preprint arXiv:2507.19230 (2025)","DOI":"10.1007\/978-3-032-16128-4_2"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Rokuss, M., et al.: Lesionlocator: zero-shot universal tumor segmentation and tracking in 3d whole-body imaging. In: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), pp. 30872\u201330885 (2025)","DOI":"10.1109\/CVPR52734.2025.02875"},{"key":"25_CR16","unstructured":"Rokuss, M., et al.: Voxtell: free-text promptable universal 3d medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2026)"},{"key":"25_CR17","doi-asserted-by":"publisher","unstructured":"Rokuss, M.R., et al.: Longitudinal segmentation of ms lesions via temporal difference weighting. In: Schroder, A., et al. (eds.) MICCAI 2024. LNCS, vol. 15401. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-84525-3_6","DOI":"10.1007\/978-3-031-84525-3_6"},{"key":"25_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102675","volume":"83","author":"A Szeskin","year":"2023","unstructured":"Szeskin, A., Rochman, S., Weiss, S., Lederman, R., Sosna, J., Joskowicz, L.: Liver lesion changes analysis in longitudinal cect scans by simultaneous deep learning voxel classification with simu-net. Med. Image Anal. 83, 102675 (2023)","journal-title":"Med. Image Anal."},{"key":"25_CR19","doi-asserted-by":"publisher","unstructured":"Tian, L., et al.: unigradicon: a foundation model for medical image registration In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15002. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72069-7_70","DOI":"10.1007\/978-3-031-72069-7_70"},{"key":"25_CR20","doi-asserted-by":"publisher","unstructured":"Vizitiu, A., Mohaiu, A.T., Popdan, I.M., Balachandran, A., Ghesu, F.C., Comaniciu, D.: Multi-scale self-supervised learning for longitudinal lesion tracking with optional supervision. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14220. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43907-0_55","DOI":"10.1007\/978-3-031-43907-0_55"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Wong, H.E., Rakic, M., Guttag, J., Dalca, A.V.: Scribbleprompt: fast and flexible interactive segmentation for any biomedical image (2024). https:\/\/arxiv.org\/abs\/2312.07381","DOI":"10.1007\/978-3-031-73661-2_12"},{"key":"25_CR22","doi-asserted-by":"publisher","unstructured":"Wu, Y., Wu, Z., Shi, H., Picker, B., Chong, W., Cai, J.: Coactseg: learning from heterogeneous data for new multiple sclerosis lesion segmentation. In: Greenspan, H., et al. MICCAI 2023. LNCS, vol. 14227. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43993-3_1","DOI":"10.1007\/978-3-031-43993-3_1"},{"issue":"10","key":"25_CR23","doi-asserted-by":"publisher","first-page":"2658","DOI":"10.1109\/TMI.2022.3169003","volume":"41","author":"K Yan","year":"2022","unstructured":"Yan, K., et al.: Sam: self-supervised learning of pixel-wise anatomical embeddings in radiological images. IEEE Trans. Med. Imaging 41(10), 2658\u20132669 (2022)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-38172-9_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T15:17:13Z","timestamp":1790176633000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-38172-9_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,24]]},"ISBN":["9783032381712","9783032381729"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-38172-9_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,24]]},"assertion":[{"value":"24 September 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare.","order":1,"name":"Ethics","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2026\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}