{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:36:33Z","timestamp":1779384993558,"version":"3.53.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051615","type":"print"},{"value":"9783032051622","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"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\/978-3-032-05162-2_18","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T23:27:24Z","timestamp":1758238044000},"page":"183-193","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CAPE: Connectivity-Aware Path Enforcement Loss for Curvilinear Structure Delineation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5610-2604","authenticated-orcid":false,"given":"Elyar","family":"Esmaeilzadeh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2720-7415","authenticated-orcid":false,"given":"Ehsan","family":"Garaaghaji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3882-4275","authenticated-orcid":false,"given":"Farzad","family":"Hallaji Azad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9403-4628","authenticated-orcid":false,"given":"Doruk","family":"Oner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Breitenreicher, D., Sofka, M., Britzen, S., Zhou, S.K.: Hierarchical discriminative framework for detecting tubular structures in 3D images. In: Information Processing in Medical Imaging, pp. 328\u2013339 (2013)","DOI":"10.1007\/978-3-642-38868-2_28"},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Byrne, N., Clough, J.R., Montana, G., King, A.P.: A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI. In: Statistical Atlases and Computational Models of the Heart. M and Ms and EMIDEC Challenges, pp. 3\u201313 (2021)","DOI":"10.1007\/978-3-030-68107-4_1"},{"key":"18_CR3","doi-asserted-by":"publisher","first-page":"8766","DOI":"10.1109\/TPAMI.2020.3013679","volume":"44","author":"JR Clough","year":"2022","unstructured":"Clough, J.R., et al.: A topological loss function for deep-learning based image segmentation using persistent homology. IEEE Trans. Pattern Anal. Mach. Intell. 44, 8766\u20138778 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Edelsbrunner, H., Harer, J.: Persistent homology \u2014 a survey. In: Surveys on discrete and computational geometry, vol.\u00a0453, p.\u00a0257. Amer Mathematical Society (2008)","DOI":"10.1090\/conm\/453\/08802"},{"key":"18_CR5","unstructured":"Etten, A.: Spacenet road detection and routing challenge part II\u2014APLS implementation (2017)"},{"key":"18_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1007\/BFb0056195","volume-title":"Medical Image Computing and Computer-Assisted Interventation \u2014 MICCAI\u201998","author":"AF Frangi","year":"1998","unstructured":"Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130\u2013137. Springer, Heidelberg (1998). https:\/\/doi.org\/10.1007\/BFb0056195"},{"issue":"7","key":"18_CR7","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.1109\/TPAMI.2018.2835450","volume":"41","author":"J Funke","year":"2018","unstructured":"Funke, J.: 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."},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Ganin, Y., Lempitsky, V.: $$n^4$$-fields: Neural network nearest neighbor fields for image transforms. In: Computer Vision \u2013 ACCV, pp. 536\u2013551 (2015)","DOI":"10.1007\/978-3-319-16808-1_36"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Guo, Z., et al.: Deepcenterline: a multi-task fully convolutional network for centerline extraction. In: Information Processing in Medical Imaging, pp. 441\u2013453 (2019)","DOI":"10.1007\/978-3-030-20351-1_34"},{"key":"18_CR10","unstructured":"Hu, X., Li, F., Samaras, D., Chen, C.: Topology-preserving deep image segmentation. In: Advances in Neural Information Processing Systems, vol.\u00a032 (2019)"},{"issue":"8","key":"18_CR11","doi-asserted-by":"publisher","first-page":"1977","DOI":"10.1080\/01431160802546837","volume":"30","author":"X Huang","year":"2009","unstructured":"Huang, X., Zhang, L.: Road centreline extraction from high-resolution imagery based on multiscale structural features and support vector machines. Int. J. Remote Sens. 30(8), 1977\u20131987 (2009)","journal-title":"Int. J. Remote Sens."},{"key":"18_CR12","unstructured":"Kingma, D., Ba, J.: Adam: A method for stochastic optimization. In: International Conference on Learning Representations (2015)"},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Law, M.W.K., Chung, A.C.S.: Three dimensional curvilinear structure detection using optimally oriented flux. In: Computer Vision \u2013 ECCV, pp. 368\u2013382 (2008)","DOI":"10.1007\/978-3-540-88693-8_27"},{"key":"18_CR14","doi-asserted-by":"crossref","unstructured":"Maninis, K.K., Pont-Tuset, J., Arbel\u00e1ez, P., Van\u00a0Gool, L.: Deep retinal image understanding. In: Medical Image Computing and Computer-Assisted Intervention, pp. 140\u2013148 (2016)","DOI":"10.1007\/978-3-319-46723-8_17"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Mnih, V., Hinton, G.E.: Learning to detect roads in high-resolution aerial images. In: Computer Vision \u2013 ECCV, pp. 210\u2013223 (2010)","DOI":"10.1007\/978-3-642-15567-3_16"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Mosinska, A., M\u00e1rquez-Neila, P., Kozi\u0144ski, M., Fua, P.: Beyond the pixel-wise loss for topology-aware delineation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3136\u20133145 (2017)","DOI":"10.1109\/CVPR.2018.00331"},{"issue":"8","key":"18_CR17","doi-asserted-by":"publisher","first-page":"10588","DOI":"10.1109\/TPAMI.2023.3246921","volume":"45","author":"D Oner","year":"2023","unstructured":"Oner, D., Garin, A., Kozi\u0144ski, M., Hess, K., Fua, P.: Persistent homology with improved locality information for more effective delineation. IEEE Trans. Pattern Anal. Mach. Intell. 45(8), 10588\u201310595 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"09","key":"18_CR18","first-page":"5401","volume":"44","author":"D Oner","year":"2022","unstructured":"Oner, D.: Promoting connectivity of network-like structures by enforcing region separation. IEEE Trans. Pattern Anal. Mach. Intell. 44(09), 5401\u20135413 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Oner, D., Osman, H., Kozi\u0144ski, M., Fua, P.: Enforcing connectivity of 3D linear structures using their 2d projections. In: Medical Image Computing and Computer Assisted Intervention 2022, pp. 591\u2013601 (2022)","DOI":"10.1007\/978-3-031-16443-9_57"},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Peng, H., Zhou, Z., Meijering, E.e.a.: Automatic tracing of ultra-volumes of neuronal images. In: Nature Methods 14 (2017)","DOI":"10.1038\/nmeth.4233"},{"key":"18_CR21","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention 2015, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"18_CR22","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"},{"key":"18_CR23","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)"},{"key":"18_CR24","doi-asserted-by":"crossref","unstructured":"Sironi, A., Lepetit, V., Fua, P.: Multiscale centerline detection by learning a scale-space distance transform. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014)","DOI":"10.1109\/CVPR.2014.351"},{"issue":"4","key":"18_CR25","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"J Staal","year":"2004","unstructured":"Staal, J., Abr\u00e0moff, M.D., Niemeijer, M., Viergever, M.A., 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":"18_CR26","doi-asserted-by":"crossref","unstructured":"Turetken, E., Becker, C., Glowacki, P., Benmansour, F., Fua, P.: Detecting irregular curvilinear structures in gray scale and color imagery using multi-directional oriented flux. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1553\u20131560. Institute of Electrical and Electronics Engineers Inc. (2013)","DOI":"10.1109\/ICCV.2013.196"},{"issue":"12","key":"18_CR27","doi-asserted-by":"publisher","first-page":"2515","DOI":"10.1109\/TPAMI.2016.2519025","volume":"38","author":"E T\u00fcretken","year":"2016","unstructured":"T\u00fcretken, E.: Reconstructing curvilinear networks using path classifiers and integer programming. IEEE Trans. Pattern Anal. Mach. Intell. 38(12), 2515\u20132530 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"18_CR28","doi-asserted-by":"crossref","unstructured":"Wegner, J.D., Montoya-Zegarra, J.A., Schindler, K.: A higher-order CRF model for road network extraction. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1698\u20131705 (2013)","DOI":"10.1109\/CVPR.2013.222"},{"key":"18_CR29","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.media.2018.10.005","volume":"51","author":"JM Wolterink","year":"2019","unstructured":"Wolterink, J.M., van Hamersvelt, R.W., Viergever, M.A., Leiner, T., I\u0161gum, I.: Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier. Med. Image Anal. 51, 46\u201360 (2019)","journal-title":"Med. Image Anal."},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"Wu, D., et al.: A learning based deformable template matching method for automatic rib centerline extraction and labeling in CT images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 980\u2013987 (2012)","DOI":"10.1109\/CVPR.2012.6247774"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05162-2_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T23:27:34Z","timestamp":1758238054000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05162-2_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032051615","9783032051622"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05162-2_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,19]]},"assertion":[{"value":"19 September 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":"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":"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":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}