{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T18:01:00Z","timestamp":1777658460003,"version":"3.51.4"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031966279","type":"print"},{"value":"9783031966286","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T00:00:00Z","timestamp":1754179200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T00:00:00Z","timestamp":1754179200000},"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\/978-3-031-96628-6_20","type":"book-chapter","created":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T07:40:53Z","timestamp":1754120453000},"page":"297-312","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Pitfalls of\u00a0Topology-Aware Image Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8843-7684","authenticated-orcid":false,"given":"Alexander H.","family":"Berger","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7359-6212","authenticated-orcid":false,"given":"Laurin","family":"Lux","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Weers","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8261-7810","authenticated-orcid":false,"given":"Martin J.","family":"Menten","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5683-5889","authenticated-orcid":false,"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4844-6955","authenticated-orcid":false,"given":"Johannes C.","family":"Paetzold","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"issue":"5","key":"20_CR1","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","volume":"33","author":"P Arbelaez","year":"2010","unstructured":"Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898\u2013916 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR2","doi-asserted-by":"publisher","DOI":"10.3389\/fnana.2015.00142","volume":"9","author":"I Arganda-Carreras","year":"2015","unstructured":"Arganda-Carreras, I., et al.: Crowdsourcing the creation of image segmentation algorithms for connectomics. Front. Neuroanat. 9, 152591 (2015)","journal-title":"Front. Neuroanat."},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Attari, M., Nguyen, N.P., Palaniappan, K., Bunyak, F.: Multi-loss topology-aware deep learning network for segmentation of vessels in microscopy images. In: 2023 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp.\u00a01\u20137. IEEE (2023)","DOI":"10.1109\/AIPR60534.2023.10440665"},{"issue":"10","key":"20_CR4","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1038\/s42256-020-0227-9","volume":"2","author":"S Banerjee","year":"2020","unstructured":"Banerjee, S., et al.: Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks. Nat. Mach. Intell. 2(10), 585\u2013594 (2020)","journal-title":"Nat. Mach. Intell."},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Berger, A.H., et al.: Topologically faithful multi-class segmentation in medical images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 721\u2013731. Springer (2024)","DOI":"10.1007\/978-3-031-72111-3_68"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Bleile, B., Garin, A., Heiss, T., Maggs, K., Robins, V.: The persistent homology of dual digital image constructions. In: Research in Computational Topology 2, pp. 1\u201326. Springer (2022)","DOI":"10.1007\/978-3-030-95519-9_1"},{"key":"20_CR7","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1109\/RBME.2021.3136343","volume":"16","author":"S Bohlender","year":"2021","unstructured":"Bohlender, S., Oksuz, I., Mukhopadhyay, A.: A survey on shape-constraint deep learning for medical image segmentation. IEEE Rev. Biomed. Eng. 16, 225\u2013240 (2021)","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"1","key":"20_CR8","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1109\/TMI.2022.3203309","volume":"42","author":"N Byrne","year":"2022","unstructured":"Byrne, N., Clough, J.R., Valverde, I., Montana, G., King, A.P.: A persistent homology-based topological loss for CNN-based multiclass segmentation of CMR. IEEE Trans. Med. Imaging 42(1), 3\u201314 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"20_CR9","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.neuroimage.2016.12.064","volume":"148","author":"A Carass","year":"2017","unstructured":"Carass, A., et al.: Longitudinal multiple sclerosis lesion segmentation: resource and challenge. Neuroimage 148, 77\u2013102 (2017)","journal-title":"Neuroimage"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Christodoulou, E., et\u00a0al.: Confidence intervals uncovered: are we ready for real-world medical imaging AI? In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 124\u2013132. Springer (2024)","DOI":"10.1007\/978-3-031-72117-5_12"},{"issue":"12","key":"20_CR11","doi-asserted-by":"publisher","first-page":"8766","DOI":"10.1109\/TPAMI.2020.3013679","volume":"44","author":"JR Clough","year":"2020","unstructured":"Clough, J.R., Byrne, N., Oksuz, I., Zimmer, V.A., Schnabel, J.A., King, A.P.: A topological loss function for deep-learning based image segmentation using persistent homology. IEEE Trans. Pattern Anal. Mach. Intell. 44(12), 8766\u20138778 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR12","unstructured":"Commowick, O., Cervenansky, F., Cotton, F., Dojat, M.: MSSEG-2 challenge proceedings: multiple sclerosis new lesions segmentation challenge using a data management and processing infrastructure. In: MICCAI 2021-24th International Conference on Medical Image Computing and Computer Assisted Intervention, p.\u00a0126 (2021)"},{"key":"20_CR13","unstructured":"Dosovitskiy, A.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"20_CR14","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.ymeth.2016.12.013","volume":"115","author":"J Funke","year":"2017","unstructured":"Funke, J., Klein, J., Moreno-Noguer, F., Cardona, A., Cook, M.: TED: a tolerant edit distance for segmentation evaluation. Methods 115, 119\u2013127 (2017)","journal-title":"Methods"},{"issue":"7","key":"20_CR15","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.1109\/TPAMI.2018.2835450","volume":"41","author":"J Funke","year":"2018","unstructured":"Funke, J., et al.: Large scale image segmentation with structured loss based deep learning for connectome reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1669\u20131680 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"20_CR16","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1109\/TMI.2011.2167982","volume":"31","author":"ME Geg\u00fandez-Arias","year":"2011","unstructured":"Geg\u00fandez-Arias, M.E., Aquino, A., Bravo, J.M., Mar\u00edn, D.: A function for quality evaluation of retinal vessel segmentations. IEEE Trans. Med. Imaging 31(2), 231\u2013239 (2011)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"20_CR17","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/42.993130","volume":"21","author":"X Han","year":"2002","unstructured":"Han, X., Xu, C., Braga-Neto, U., Prince, J.L.: Topology correction in brain cortex segmentation using a multiscale, graph-based algorithm. IEEE Trans. Med. Imaging 21(2), 109\u2013121 (2002)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"20_CR18","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1109\/42.845178","volume":"19","author":"A Hoover","year":"2000","unstructured":"Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203\u2013210 (2000)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"20_CR19","unstructured":"Hu, X., Wang, Y., Li, F., Samaras, D., Chen, C.: Topology-aware segmentation using discrete Morse theory. In: International Conference on Learning Representations (ICLR) (2021)"},{"key":"20_CR20","first-page":"24046","volume":"35","author":"X Hu","year":"2022","unstructured":"Hu, X.: Structure-aware image segmentation with homotopy warping. Adv. Neural. Inf. Process. Syst. 35, 24046\u201324059 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"20_CR21","unstructured":"Hu, X., Li, F., Samaras, D., Chen, C.: Topology-preserving deep image segmentation. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"20_CR22","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF01908075","volume":"2","author":"L Hubert","year":"1985","unstructured":"Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2, 193\u2013218 (1985)","journal-title":"J. Classif."},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Isensee, F., et al.: nnU-Net revisited: a call for rigorous validation in 3d medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 488\u2013498. Springer (2024)","DOI":"10.1007\/978-3-031-72114-4_47"},{"issue":"8","key":"20_CR24","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1038\/s41592-018-0049-4","volume":"15","author":"M Januszewski","year":"2018","unstructured":"Januszewski, M., et al.: High-precision automated reconstruction of neurons with flood-filling networks. Nat. Methods 15(8), 605\u2013610 (2018)","journal-title":"Nat. Methods"},{"issue":"1","key":"20_CR25","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1038\/s41597-022-01564-3","volume":"9","author":"K Jin","year":"2022","unstructured":"Jin, K., et al.: FIVES: a fundus image dataset for artificial intelligence based vessel segmentation. Sci. Data 9(1), 475 (2022)","journal-title":"Sci. Data"},{"issue":"3","key":"20_CR26","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1016\/j.cell.2015.06.054","volume":"162","author":"N Kasthuri","year":"2015","unstructured":"Kasthuri, N., et al.: Saturated reconstruction of a volume of neocortex. Cell 162(3), 648\u2013661 (2015)","journal-title":"Cell"},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Li, L., et\u00a0al.: Robust segmentation via topology violation detection and feature synthesis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 67\u201377. Springer (2023)","DOI":"10.1007\/978-3-031-43901-8_7"},{"key":"20_CR28","doi-asserted-by":"crossref","unstructured":"Li, L., et al.: Universal topology refinement for medical image segmentation with polynomial feature synthesis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 670\u2013680. Springer (2024)","DOI":"10.1007\/978-3-031-72114-4_64"},{"key":"20_CR29","unstructured":"Lux, L., et al.: Topograph: an efficient graph-based framework for strictly topology preserving image segmentation. arXiv preprint arXiv:2411.03228 (2024)"},{"issue":"3","key":"20_CR30","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1109\/TMI.2020.3042802","volume":"40","author":"Y Ma","year":"2020","unstructured":"Ma, Y., et al.: ROSE: a retinal oct-angiography vessel segmentation dataset and new model. IEEE Trans. Med. Imaging 40(3), 928\u2013939 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"20_CR31","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/s41592-023-02151-z","volume":"21","author":"L Maier-Hein","year":"2024","unstructured":"Maier-Hein, L., et al.: Metrics reloaded: recommendations for image analysis validation. Nat. Methods 21(2), 195\u2013212 (2024)","journal-title":"Nat. Methods"},{"key":"20_CR32","doi-asserted-by":"crossref","unstructured":"Meil\u0103, M.: Comparing clusterings by the variation of information. In: Learning Theory and Kernel Machines: 16th Annual Conference on Learning Theory and 7th Kernel Workshop, COLT\/Kernel 2003, Washington, DC, USA, 24\u201327 August 2003. Proceedings, pp. 173\u2013187. Springer (2003)","DOI":"10.1007\/978-3-540-45167-9_14"},{"issue":"5","key":"20_CR33","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1016\/j.jmva.2006.11.013","volume":"98","author":"M Meil\u0103","year":"2007","unstructured":"Meil\u0103, M.: Comparing clusterings\u2013an information based distance. J. Multivar. Anal. 98(5), 873\u2013895 (2007)","journal-title":"J. Multivar. Anal."},{"key":"20_CR34","doi-asserted-by":"crossref","unstructured":"Meil\u0103, M.: Comparing clusterings: an axiomatic view. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 577\u2013584 (2005)","DOI":"10.1145\/1102351.1102424"},{"key":"20_CR35","unstructured":"Mnih, V.: Machine learning for aerial image labeling. Ph.D. thesis, CAN (2013). aAINR96184"},{"key":"20_CR36","doi-asserted-by":"crossref","unstructured":"Mosinska, A., Marquez-Neila, P., Kozi\u0144ski, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136\u20133145 (2018)","DOI":"10.1109\/CVPR.2018.00331"},{"issue":"8","key":"20_CR37","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0071715","volume":"8","author":"J Nunez-Iglesias","year":"2013","unstructured":"Nunez-Iglesias, J., Kennedy, R., Parag, T., Shi, J., Chklovskii, D.B.: Machine learning of hierarchical clustering to segment 2d and 3d images. PLoS ONE 8(8), e71715 (2013)","journal-title":"PLoS ONE"},{"issue":"7","key":"20_CR38","doi-asserted-by":"publisher","first-page":"1837","DOI":"10.1109\/JBHI.2020.2991043","volume":"24","author":"AS Panayides","year":"2020","unstructured":"Panayides, A.S., et al.: Ai in medical imaging informatics: current challenges and future directions. IEEE J. Biomed. Health Inform. 24(7), 1837\u20131857 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"2","key":"20_CR39","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1038\/s41592-023-02150-0","volume":"21","author":"A Reinke","year":"2024","unstructured":"Reinke, A., et al.: Understanding metric-related pitfalls in image analysis validation. Nat. Methods 21(2), 182\u2013194 (2024)","journal-title":"Nat. Methods"},{"key":"20_CR40","doi-asserted-by":"publisher","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, Part III. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"20_CR41","doi-asserted-by":"crossref","unstructured":"Shit, S., et al.: clDice-a novel topology-preserving loss function for tubular structure segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16560\u201316569 (2021)","DOI":"10.1109\/CVPR46437.2021.01629"},{"issue":"4","key":"20_CR42","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"J Staal","year":"2004","unstructured":"Staal, J., Abramoff, M., Niemeijer, M., Viergever, M., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501\u2013509 (2004)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"20_CR43","unstructured":"Stucki, N., Paetzold, J.C., Shit, S., Menze, B., Bauer, U.: Topologically faithful image segmentation via induced matching of persistence barcodes. In: International Conference on Machine Learning, pp. 32698\u201332727. PMLR (2023)"},{"issue":"4","key":"20_CR44","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1038\/s41592-020-0792-1","volume":"17","author":"MI Todorov","year":"2020","unstructured":"Todorov, M.I., Piraud, M., et al.: Machine learning analysis of whole mouse brain vasculature. Nat. Methods 17(4), 442\u2013449 (2020)","journal-title":"Nat. Methods"},{"issue":"6","key":"20_CR45","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1109\/TPAMI.2007.1046","volume":"29","author":"R Unnikrishnan","year":"2007","unstructured":"Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward objective evaluation of image segmentation algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 929\u2013944 (2007)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR46","doi-asserted-by":"crossref","unstructured":"Wu, Q., Chen, Y., Liu, W., Yue, X., Zhuang, X.: Deep closing: enhancing topological connectivity in medical tubular segmentation. IEEE Trans. Med. Imag. (2024)","DOI":"10.1109\/TMI.2024.3405982"},{"issue":"3","key":"20_CR47","doi-asserted-by":"publisher","first-page":"036501","DOI":"10.1117\/1.JMI.5.3.036501","volume":"5","author":"K Yan","year":"2018","unstructured":"Yan, K., Wang, X., Lu, L., Summers, R.M.: DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J. Med. Imaging 5(3), 036501 (2018)","journal-title":"J. Med. Imaging"},{"key":"20_CR48","unstructured":"Yang, K., et\u00a0al.: Benchmarking the CoW with the TopCoW challenge: topology-aware anatomical segmentation of the circle of Willis for CTA and MRA. arXiv preprint arXiv:2312.17670 (2023)"},{"key":"20_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: Geometric loss for deep multiple sclerosis lesion segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 24\u201328. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9434085"}],"container-title":["Lecture Notes in Computer Science","Information Processing in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-96628-6_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T12:12:18Z","timestamp":1757333538000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-96628-6_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"ISBN":["9783031966279","9783031966286"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-96628-6_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"3 August 2025","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":"IPMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Processing in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"25 May 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 May 2025","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":"ipmi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ipmi2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}