{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T03:44:51Z","timestamp":1770349491055,"version":"3.49.0"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031721106","type":"print"},{"value":"9783031721113","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72111-3_15","type":"book-chapter","created":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T21:01:34Z","timestamp":1728162094000},"page":"155-165","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["CUTS: A Deep Learning and\u00a0Topological Framework for\u00a0Multigranular Unsupervised Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Chen","family":"Liu","sequence":"first","affiliation":[]},{"given":"Matthew","family":"Amodio","sequence":"additional","affiliation":[]},{"given":"Liangbo L.","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Arman","family":"Avesta","sequence":"additional","affiliation":[]},{"given":"Sanjay","family":"Aneja","sequence":"additional","affiliation":[]},{"given":"Jay C.","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Lucian V.","family":"Del Priore","sequence":"additional","affiliation":[]},{"given":"Smita","family":"Krishnaswamy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,6]]},"reference":[{"issue":"1","key":"15_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-018-0040-6","volume":"1","author":"MD Abr\u00e0moff","year":"2018","unstructured":"Abr\u00e0moff, M.D., Lavin, P.T., Birch, M., Shah, N., Folk, J.C.: Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit. Med. 1(1), 1\u20138 (2018)","journal-title":"NPJ Digit. Med."},{"issue":"11","key":"15_CR2","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","volume":"34","author":"R Achanta","year":"2012","unstructured":"Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., S\u00fcsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274\u20132282 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Brugnone, N., et al.: Coarse graining of data via inhomogeneous diffusion condensation. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 2624\u20132633. IEEE (2019)","DOI":"10.1109\/BigData47090.2019.9006013"},{"issue":"3","key":"15_CR4","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.neurobiolaging.2006.01.006","volume":"28","author":"OT Carmichael","year":"2007","unstructured":"Carmichael, O.T., et al.: Ventricular volume and dementia progression in the cardiovascular health study. Neurobiol. Aging 28(3), 389\u2013397 (2007)","journal-title":"Neurobiol. Aging"},{"key":"15_CR5","unstructured":"Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Contrastive learning of global and local features for medical image segmentation with limited annotations. arXiv:2006.10511 (2020)"},{"key":"15_CR6","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. arXiv:2002.05709 (2020)"},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750\u201315758 (2021)","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"15_CR8","unstructured":"Cheng, J., et al.: SAM-Med2D. arXiv preprint arXiv:2308.16184 (2023)"},{"key":"15_CR9","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1016\/j.neuroimage.2015.04.067","volume":"124","author":"KL Crawford","year":"2016","unstructured":"Crawford, K.L., Neu, S.C., Toga, A.W.: The image and data archive at the laboratory of neuro imaging. Neuroimage 124, 1080\u20131083 (2016)","journal-title":"Neuroimage"},{"issue":"11","key":"15_CR10","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1001\/archopht.123.11.1484","volume":"123","author":"MD Davis","year":"2005","unstructured":"Davis, M.D., et al.: The age-related eye disease study severity scale for age-related macular degeneration: AREDS report no. 17. Arch. Ophthalmol. 123(11), 1484\u20131498 (2005)","journal-title":"Arch. Ophthalmol."},{"issue":"1","key":"15_CR11","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","volume":"25","author":"A Esteva","year":"2019","unstructured":"Esteva, A., et al.: A guide to deep learning in healthcare. Nat. Med. 25(1), 24\u201329 (2019)","journal-title":"Nat. Med."},{"issue":"2","key":"15_CR12","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1023\/B:VISI.0000022288.19776.77","volume":"59","author":"PF Felzenszwalb","year":"2004","unstructured":"Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vision 59(2), 167\u2013181 (2004)","journal-title":"Int. J. Comput. Vision"},{"key":"15_CR13","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.ejmp.2021.05.003","volume":"85","author":"Y Fu","year":"2021","unstructured":"Fu, Y., Lei, Y., Wang, T., Curran, W.J., Liu, T., Yang, X.: A review of deep learning based methods for medical image multi-organ segmentation. Physica Med. 85, 107\u2013122 (2021)","journal-title":"Physica Med."},{"key":"15_CR14","unstructured":"Hamilton, M., Zhang, Z., Hariharan, B., Snavely, N., Freeman, W.T.: Unsupervised semantic segmentation by distilling feature correspondences. In: International Conference on Learning Representations (2022)"},{"key":"15_CR15","doi-asserted-by":"publisher","first-page":"100297","DOI":"10.1016\/j.imu.2020.100297","volume":"18","author":"IRI Haque","year":"2020","unstructured":"Haque, I.R.I., Neubert, J.: Deep learning approaches to biomedical image segmentation. Inf. Med. Unlocked 18, 100297 (2020)","journal-title":"Inf. Med. Unlocked"},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: 2010 20th International Conference on Pattern Recognition, pp. 2366\u20132369. IEEE (2010)","DOI":"10.1109\/ICPR.2010.579"},{"issue":"2","key":"15_CR17","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"issue":"7","key":"15_CR18","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1109\/TMI.2008.2011480","volume":"28","author":"I Isgum","year":"2009","unstructured":"Isgum, I., Staring, M., Rutten, A., Prokop, M., Viergever, M.A., Van Ginneken, B.: Multi-atlas-based segmentation with local decision fusion-application to cardiac and aortic segmentation in CT scans. IEEE Trans. Med. Imaging 28(7), 1000\u20131010 (2009)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"15_CR19","doi-asserted-by":"publisher","first-page":"8055","DOI":"10.1109\/TIP.2020.3011269","volume":"29","author":"W Kim","year":"2020","unstructured":"Kim, W., Kanezaki, A., Tanaka, M.: Unsupervised learning of image segmentation based on differentiable feature clustering. IEEE Trans. Image Process. 29, 8055\u20138068 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et al.: Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134026 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"issue":"1","key":"15_CR21","doi-asserted-by":"publisher","first-page":"2589","DOI":"10.1038\/s41467-023-37025-7","volume":"14","author":"M Kuchroo","year":"2023","unstructured":"Kuchroo, M., et al.: Single-cell analysis reveals inflammatory interactions driving macular degeneration. Nat. Commun. 14(1), 2589 (2023)","journal-title":"Nat. Commun."},{"issue":"5","key":"15_CR22","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1038\/s41587-021-01186-x","volume":"40","author":"M Kuchroo","year":"2022","unstructured":"Kuchroo, M., et al.: Multiscale phate identifies multimodal signatures of COVID-19. Nat. Biotechnol. 40(5), 681\u2013691 (2022)","journal-title":"Nat. Biotechnol."},{"issue":"1","key":"15_CR23","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nat. Commun. 15(1), 654 (2024)","journal-title":"Nat. Commun."},{"key":"15_CR24","doi-asserted-by":"publisher","first-page":"102918","DOI":"10.1016\/j.media.2023.102918","volume":"89","author":"MA Mazurowski","year":"2023","unstructured":"Mazurowski, M.A., Dong, H., Gu, H., Yang, J., Konz, N., Zhang, Y.: Segment anything model for medical image analysis: an experimental study. Med. Image Anal. 89, 102918 (2023)","journal-title":"Med. Image Anal."},{"issue":"2","key":"15_CR25","doi-asserted-by":"publisher","first-page":"647","DOI":"10.3233\/JAD-2010-1406","volume":"20","author":"BR Ott","year":"2010","unstructured":"Ott, B.R., et al.: Brain ventricular volume and cerebrospinal fluid biomarkers of alzheimer\u2019s disease. J. Alzheimers Dis. 20(2), 647\u2013657 (2010)","journal-title":"J. Alzheimers Dis."},{"key":"15_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1007\/978-3-030-58526-6_45","volume-title":"Computer Vision \u2013 ECCV 2020","author":"C Ouyang","year":"2020","unstructured":"Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., Rueckert, D.: Self-supervision with superpixels: training few-shot medical image segmentation without annotation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 762\u2013780. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58526-6_45"},{"key":"15_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"8","key":"15_CR28","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1016\/j.oret.2020.11.003","volume":"5","author":"LL Shen","year":"2021","unstructured":"Shen, L.L., et al.: Relationship of topographic distribution of geographic atrophy to visual acuity in nonexudative age-related macular degeneration. Ophthalmol. Retina 5(8), 761\u2013774 (2021)","journal-title":"Ophthalmol. Retina"},{"issue":"06","key":"15_CR29","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/34.87344","volume":"13","author":"L Vincent","year":"1991","unstructured":"Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13(06), 583\u2013598 (1991)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"15_CR30","doi-asserted-by":"crossref","unstructured":"Wang, S., et al.: LT-Net: label transfer by learning reversible voxel-wise correspondence for one-shot medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9162\u20139171 (2020)","DOI":"10.1109\/CVPR42600.2020.00918"},{"key":"15_CR31","unstructured":"Yan, K., et al.: SAM: self-supervised learning of pixel-wise anatomical embeddings in radiological images. arXiv:2012.02383 (2020)"},{"key":"15_CR32","unstructured":"Zha, H., He, X., Ding, C., Gu, M., Simon, H.: Spectral relaxation for k-means clustering. In: Advances in Neural Information Processing Systems, vol. 14 (2001)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72111-3_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T21:03:22Z","timestamp":1728162202000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72111-3_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721106","9783031721113"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72111-3_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"6 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}