{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:14:24Z","timestamp":1767320064385,"version":"3.48.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032095121","type":"print"},{"value":"9783032095138","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\/978-3-032-09513-8_7","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:09:59Z","timestamp":1767319799000},"page":"63-73","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Regional Hausdorff Distance Losses for\u00a0Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Lisa","family":"Guzzi","sequence":"first","affiliation":[]},{"given":"Maria A.","family":"Zuluaga","sequence":"additional","affiliation":[]},{"given":"Riccardo","family":"Taiello","sequence":"additional","affiliation":[]},{"given":"Fabien","family":"Lareyre","sequence":"additional","affiliation":[]},{"given":"Gilles","family":"Di Lorenzo","sequence":"additional","affiliation":[]},{"given":"S\u00e9bastien","family":"Goffart","sequence":"additional","affiliation":[]},{"given":"Andrea","family":"Chierici","sequence":"additional","affiliation":[]},{"given":"Juliette","family":"Raffort","sequence":"additional","affiliation":[]},{"given":"Herv\u00e9","family":"Delingette","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"issue":"1","key":"7_CR1","doi-asserted-by":"publisher","first-page":"4128","DOI":"10.1038\/s41467-022-30695-9","volume":"13","author":"M Antonelli","year":"2022","unstructured":"Antonelli, M., et al.: The medical segmentation decathlon. Nat. Commun. 13(1), 4128 (2022)","journal-title":"Nat. Commun."},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Bai, M., Urtasun, R.: Deep watershed transform for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5221\u20135229 (2017)","DOI":"10.1109\/CVPR.2017.305"},{"issue":"11","key":"7_CR3","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard, O., et al.: Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514\u20132525 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"7_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/978-3-030-33391-1_28","volume-title":"Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data","author":"TD Bui","year":"2019","unstructured":"Bui, T.D., Wang, L., Chen, J., Lin, W., Li, G., Shen, D.: Multi-task learning for neonatal brain segmentation using 3D dense-unet with dense attention guided by geodesic distance. In: Wang, Q., et al. (eds.) DART\/MIL3ID -2019. LNCS, vol. 11795, pp. 243\u2013251. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33391-1_28"},{"key":"7_CR5","unstructured":"Caliva, F., Iriondo, C., Martinez, A.M., Majumdar, S., Pedoia, V.: Distance map loss penalty term for semantic segmentation (2019)"},{"issue":"11","key":"7_CR6","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.1109\/TMI.2006.880587","volume":"25","author":"W Crum","year":"2006","unstructured":"Crum, W., Camara, O., Hill, D.: Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans. Med. Imaging 25(11), 1451\u20131461 (2006)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"7_CR7","doi-asserted-by":"publisher","first-page":"5637","DOI":"10.1002\/mp.13853","volume":"46","author":"S Dangi","year":"2019","unstructured":"Dangi, S., Linte, C.A., Yaniv, Z.: A distance map regularized cnn for cardiac cine mr image segmentation. Med. Phys. 46(12), 5637\u20135651 (2019)","journal-title":"Med. Phys."},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Dubuisson, M.P., Jain, A.K.: A modified hausdorff distance for object matching. In: Proceedings of 12th International Conference on Pattern Recognition, vol.\u00a01, pp. 566\u2013568. IEEE (1994)","DOI":"10.1109\/ICPR.1994.576361"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"EL Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., Abdallah, F.: A surprisingly effective perimeter-based loss for medical image segmentation. In: Proceedings of the Fourth Conference on Medical Imaging with Deep Learning. Proceedings of Machine Learning Research, vol.\u00a0143, pp. 158\u2013167 (2021)","DOI":"10.1016\/j.cviu.2021.103248"},{"key":"7_CR10","doi-asserted-by":"publisher","unstructured":"Guzzi, L., et al.: Differentiable soft morphological filters for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 177\u2013187. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-72111-3_17","DOI":"10.1007\/978-3-031-72111-3_17"},{"issue":"9","key":"7_CR11","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1109\/34.232073","volume":"15","author":"D Huttenlocher","year":"1993","unstructured":"Huttenlocher, D., Klanderman, G., Rucklidge, W.: Comparing images using the hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 850\u2013863 (1993)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"7_CR12","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":"2","key":"7_CR13","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1109\/TMI.2019.2930068","volume":"39","author":"D Karimi","year":"2019","unstructured":"Karimi, D., Salcudean, S.E.: Reducing the hausdorff distance in medical image segmentation with convolutional neural networks. IEEE Trans. Med. Imaging 39(2), 499\u2013513 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"7_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101851","volume":"67","author":"H Kervadec","year":"2021","unstructured":"Kervadec, H., Bouchtiba, J., Desrosiers, C., Granger, E., Dolz, J., Ben Ayed, I.: Boundary loss for highly unbalanced segmentation. Med. Image Anal. 67, 101851 (2021)","journal-title":"Med. Image Anal."},{"key":"7_CR15","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.ejmp.2019.10.001","volume":"67","author":"S Moradi","year":"2019","unstructured":"Moradi, S., et al.: Mfp-unet: a novel deep learning based approach for left ventricle segmentation in echocardiography. Physica Med. 67, 58\u201369 (2019)","journal-title":"Physica Med."},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Navarro, F., et al.: Shape-aware complementary-task learning for multi-organ segmentation. In: Machine Learning in Medical Imaging, pp. 620\u2013627 (2019)","DOI":"10.1007\/978-3-030-32692-0_71"},{"key":"7_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1007\/978-3-030-71278-5_31","volume-title":"Pattern Recognition","author":"DD Pham","year":"2021","unstructured":"Pham, D.D., Dovletov, G., Pauli, J.: A differentiable convolutional distance transform layer for improved image segmentation. In: Akata, Z., Geiger, A., Sattler, T. (eds.) DAGM GCPR 2020. LNCS, vol. 12544, pp. 432\u2013444. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-71278-5_31"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Ribera, J., G era, D., Chen, Y., Delp, E.J.: Locating objects without bounding boxes (2019)","DOI":"10.1109\/CVPR.2019.00664"},{"issue":"1","key":"7_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41747-020-00189-8","volume":"4","author":"F Rizzetto","year":"2020","unstructured":"Rizzetto, F., et al.: Impact of inter-reader contouring variability on textural radiomics of colorectal liver metastases. Eur. Radiol. Exp. 4(1), 1\u201312 (2020). https:\/\/doi.org\/10.1186\/s41747-020-00189-8","journal-title":"Eur. Radiol. Exp."},{"key":"7_CR20","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 \u2014 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":"1","key":"7_CR21","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/0031-3203(68)90013-7","volume":"1","author":"A Rosenfeld","year":"1968","unstructured":"Rosenfeld, A., Pfaltz, J.L.: Distance functions on digital pictures. Pattern Recogn. 1(1), 33\u201361 (1968)","journal-title":"Pattern Recogn."},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Shit, S., et al.: clDice - a novel topology-preserving loss function for tubular structure segmentation. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16555\u201316564. IEEE Computer Society (2021)","DOI":"10.1109\/CVPR46437.2021.01629"},{"issue":"4","key":"7_CR23","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":"7_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-015-0068-x","volume":"15","author":"AA Taha","year":"2015","unstructured":"Taha, A.A., Hanbury, A.: Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15, 1\u201328 (2015)","journal-title":"BMC Med. Imaging"},{"key":"7_CR25","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Deep distance transform for tubular structure segmentation in ct scans. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3833\u20133842 (2020)","DOI":"10.1109\/CVPR42600.2020.00389"},{"key":"7_CR26","doi-asserted-by":"crossref","unstructured":"Xue, Y., et al.: Shape-aware organ segmentation by predicting signed distance maps. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 12565\u201312572 (2020)","DOI":"10.1609\/aaai.v34i07.6946"},{"key":"7_CR27","unstructured":"Yang, S., Kweon, J., Kim, Y.H.: Major vessel segmentation on x-ray coronary angiography using deep networks with a novel penalty loss function (2019)"},{"key":"7_CR28","unstructured":"Zhang, M., Yang, G.Z., Gu, Y.: Differentiable topology-preserved distance transform for pulmonary airway segmentation. arXiv preprint (2022)"},{"key":"7_CR29","doi-asserted-by":"publisher","unstructured":"Zhu, R., Oda, M., Hayashi, Y., Kitasaka, T., Mori, K.: Semi-supervised tubular structure segmentation with cross geometry and hausdorff distance consistency. In: Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024, pp. 612\u2013622. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72111-3_58","DOI":"10.1007\/978-3-031-72111-3_58"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-09513-8_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:10:01Z","timestamp":1767319801000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-09513-8_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032095121","9783032095138"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-09513-8_7","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":"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":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","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":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2025\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}