{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T06:51:40Z","timestamp":1742971900457,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031782008"},{"type":"electronic","value":"9783031782015"}],"license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-78201-5_13","type":"book-chapter","created":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T15:08:51Z","timestamp":1733065731000},"page":"194-210","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging Point Annotations in\u00a0Segmentation Learning with\u00a0Boundary Loss"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3147-5626","authenticated-orcid":false,"given":"Eva","family":"Breznik","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6786-7042","authenticated-orcid":false,"given":"Hoel","family":"Kervadec","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4980-2755","authenticated-orcid":false,"given":"Filip","family":"Malmberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8205-7569","authenticated-orcid":false,"given":"Joel","family":"Kullberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8701-969X","authenticated-orcid":false,"given":"H\u00e5kan","family":"Ahlstr\u00f6m","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6328-902X","authenticated-orcid":false,"given":"Marleen","family":"de Bruijne","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7764-1787","authenticated-orcid":false,"given":"Robin","family":"Strand","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Asad, M., Dorent, R., Vercauteren, T.: FastGeodis: fast generalised geodesic distance transform. arXiv preprint arXiv:2208.00001 (2022)","key":"13_CR1","DOI":"10.21105\/joss.04532"},{"key":"13_CR2","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s11263-008-0191-z","volume":"82","author":"X Bai","year":"2009","unstructured":"Bai, X., Sapiro, G.: Geodesic matting: a framework for fast interactive image and video segmentation and matting. Int. J. Comput. Vis. 82, 113\u2013132 (2009). https:\/\/doi.org\/10.1007\/s11263-008-0191-z","journal-title":"Int. J. Comput. Vis."},{"key":"13_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/978-3-319-46478-7_34","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Bearman","year":"2016","unstructured":"Bearman, A., Russakovsky, O., Ferrari, V., Fei-Fei, L.: What\u2019s the point: semantic segmentation with point supervision. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 549\u2013565. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_34"},{"issue":"11","key":"13_CR4","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"},{"issue":"5","key":"13_CR5","doi-asserted-by":"publisher","first-page":"1673","DOI":"10.1109\/JBHI.2020.3024262","volume":"25","author":"Z Chen","year":"2020","unstructured":"Chen, Z., et al.: Weakly supervised histopathology image segmentation with sparse point annotations. IEEE J. Biomed. Health Inform. 25(5), 1673\u20131685 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"doi-asserted-by":"crossref","unstructured":"Criminisi, A., Sharp, T., Blake, A.: GeoS: geodesic image segmentation. In: ECCV 2008, pp. 99\u2013112 (2008)","key":"13_CR6","DOI":"10.1007\/978-3-540-88682-2_9"},{"doi-asserted-by":"publisher","unstructured":"Dai, J., He, K., Sun, J.: BoxSup: exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1635\u20131643 (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.191","key":"13_CR7","DOI":"10.1109\/ICCV.2015.191"},{"key":"13_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101767","volume":"65","author":"F Dubost","year":"2020","unstructured":"Dubost, F., et al.: Weakly supervised object detection with 2D and 3D regression neural networks. Med. Image Anal. 65, 101767 (2020). https:\/\/doi.org\/10.1016\/j.media.2020.101767","journal-title":"Med. Image Anal."},{"doi-asserted-by":"crossref","unstructured":"Fan, J., Zhang, Z., Song, C., Tan, T.: Learning integral objects with intra-class discriminator for weakly-supervised semantic segmentation. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","key":"13_CR9","DOI":"10.1109\/CVPR42600.2020.00434"},{"doi-asserted-by":"publisher","unstructured":"Gulshan, V., Rother, C., Criminisi, A., Blake, A., Zisserman, A.: Geodesic star convexity for interactive image segmentation. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3129\u20133136 (2010). https:\/\/doi.org\/10.1109\/CVPR.2010.5540073","key":"13_CR10","DOI":"10.1109\/CVPR.2010.5540073"},{"issue":"2","key":"13_CR11","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"},{"doi-asserted-by":"crossref","unstructured":"Ji, Z., Shen, Y., Ma, C., Gao, M.: Scribble-based hierarchical weakly supervised learning for brain tumor segmentation. In: Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019, pp. 175\u2013183 (2019)","key":"13_CR12","DOI":"10.1007\/978-3-030-32248-9_20"},{"key":"13_CR13","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., Ayed, I.B.: Boundary loss for highly unbalanced segmentation. Med. Image Anal. 67, 101851 (2021)","journal-title":"Med. Image Anal."},{"key":"13_CR14","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.media.2019.02.009","volume":"54","author":"H Kervadec","year":"2019","unstructured":"Kervadec, H., Dolz, J., Tang, M., Granger, E., Boykov, Y., Ben Ayed, I.: Constrained-CNN losses for weakly supervised segmentation. Med. Image Anal. 54, 88\u201399 (2019)","journal-title":"Med. Image Anal."},{"unstructured":"Kervadec, H., Dolz, J., Wang, S., Granger, E., Ayed, I.B.: Bounding boxes for weakly supervised segmentation: global constraints get close to full supervision. In: Medical Imaging with Deep Learning, pp. 365\u2013381. PMLR (2020)","key":"13_CR15"},{"doi-asserted-by":"crossref","unstructured":"Kim, B., Jeong, J., Han, D., Hwang, S.J.: The devil is in the points: weakly semi-supervised instance segmentation via point-guided mask representation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11360\u201311370 (2023)","key":"13_CR16","DOI":"10.1109\/CVPR52729.2023.01093"},{"unstructured":"Kr\u00e4henb\u00fchl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: Advances in Neural Information Processing Systems 24, pp. 109\u2013117. Curran Associates, Inc. (2011)","key":"13_CR17"},{"doi-asserted-by":"crossref","unstructured":"Lin, D., Dai, J., Jia, J., He, K., Sun, J.: ScribbleSup: scribble-supervised convolutional networks for semantic segmentation. In: Computer Vision and Pattern Recognition (CVPR), pp. 3159\u20133167 (2016)","key":"13_CR18","DOI":"10.1109\/CVPR.2016.344"},{"key":"13_CR19","doi-asserted-by":"publisher","first-page":"1570","DOI":"10.1097\/HJH.0b013e3283619d50","volume":"31","author":"L Lind","year":"2013","unstructured":"Lind, L.: Relationships between three different tests to evaluate endothelium-dependent vasodilation and cardiovascular risk in a middle-aged sample. J. Hypertens. 31, 1570\u20131574 (2013). https:\/\/doi.org\/10.1097\/HJH.0b013e3283619d50","journal-title":"J. Hypertens."},{"doi-asserted-by":"crossref","unstructured":"Liu, W., He, Q., He, X.: Weakly supervised nuclei segmentation via instance learning. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp.\u00a01\u20135. IEEE (2022)","key":"13_CR20","DOI":"10.1109\/ISBI52829.2022.9761644"},{"unstructured":"Ma, J., et al.: How distance transform maps boost segmentation CNNs: an empirical study. In: Medical Imaging with Deep Learning. Proceedings of Machine Learning Research, vol.\u00a0121, pp. 479\u2013492. PMLR (2020). https:\/\/proceedings.mlr.press\/v121\/ma20b.html","key":"13_CR21"},{"key":"13_CR22","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1007\/978-3-030-32692-0_28","volume-title":"Machine Learning in Medical Imaging","author":"A Mortazi","year":"2019","unstructured":"Mortazi, A., Khosravan, N., Torigian, D.A., Kurugol, S., Bagci, U.: Weakly supervised segmentation by a deep geodesic prior. In: Suk, H.I., Liu, M., Yan, P., Lian, C. (eds.) Machine Learning in Medical Imaging, pp. 238\u2013246. Springer, Cham (2019)"},{"key":"13_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2020.102993","volume":"197\u2013198","author":"M\u00d5V Ngoc","year":"2020","unstructured":"Ngoc, M.\u00d5.V., Boutry, N., Fabrizio, J., G\u00e9raud, T.: A minimum barrier distance for multivariate images with applications. Comput. Vis. Image Underst. 197\u2013198, 102993 (2020). https:\/\/doi.org\/10.1016\/j.cviu.2020.102993","journal-title":"Comput. Vis. Image Underst."},{"doi-asserted-by":"publisher","unstructured":"Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: ENet: a deep neural network architecture for real-time semantic segmentation (2016). https:\/\/doi.org\/10.48550\/ARXIV.1606.02147","key":"13_CR24","DOI":"10.48550\/ARXIV.1606.02147"},{"unstructured":"Qu, H., et al.: Weakly supervised deep nuclei segmentation using points annotation in histopathology images. In: Medical Imaging with Deep Learning. Proceedings of Machine Learning Research, vol.\u00a0102, pp. 390\u2013400. PMLR (2019). https:\/\/proceedings.mlr.press\/v102\/qu19a.html","key":"13_CR25"},{"issue":"2","key":"13_CR26","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1109\/TMI.2016.2621185","volume":"36","author":"M Rajchl","year":"2017","unstructured":"Rajchl, M., et al.: DeepCut: object segmentation from bounding box annotations using convolutional neural networks. IEEE Trans. Med. Imaging 36(2), 674\u2013683 (2017). https:\/\/doi.org\/10.1109\/TMI.2016.2621185","journal-title":"IEEE Trans. Med. Imaging"},{"doi-asserted-by":"crossref","unstructured":"Strand, R., Ciesielski, K.C., Malmberg, F., Saha, P.K.: The minimum barrier distance. Comput. Vis. Image Underst. 117(4), 429\u2013437 (2013). Special Issue on Discrete Geometry for Computer Imagery","key":"13_CR27","DOI":"10.1016\/j.cviu.2012.10.011"},{"doi-asserted-by":"publisher","unstructured":"Tang, M., Djelouah, A., Perazzi, F., Boykov, Y., Schroers, C.: Normalized cut loss for weakly-supervised CNN segmentation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1818\u20131827 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00195","key":"13_CR28","DOI":"10.1109\/CVPR.2018.00195"},{"doi-asserted-by":"crossref","unstructured":"Tang, M., Perazzi, F., Djelouah, A., Ben Ayed, I., Schroers, C., Boykov, Y.: On regularized losses for weakly-supervised CNN segmentation. In: European Conference on Computer Vision (ECCV), Part XVI, pp. 524\u2013540 (2018)","key":"13_CR29","DOI":"10.1007\/978-3-030-01270-0_31"},{"issue":"5","key":"13_CR30","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/0167-8655(96)00010-4","volume":"17","author":"PJ Toivanen","year":"1996","unstructured":"Toivanen, P.J.: New geodesic distance transforms for gray-scale images. Pattern Recogn. Lett. 17(5), 437\u2013450 (1996). https:\/\/doi.org\/10.1016\/0167-8655(96)00010-4","journal-title":"Pattern Recogn. Lett."},{"issue":"7","key":"13_CR31","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1109\/TPAMI.2018.2840695","volume":"41","author":"G Wang","year":"2019","unstructured":"Wang, G., et al.: DeepIGeoS: a deep interactive geodesic framework for medical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1559\u20131572 (2019). https:\/\/doi.org\/10.1109\/TPAMI.2018.2840695","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"publisher","unstructured":"Xu, J., Schwing, A.G., Urtasun, R.: Learning to segment under various forms of weak supervision. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3781\u20133790 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7299002","key":"13_CR32","DOI":"10.1109\/CVPR.2015.7299002"},{"key":"13_CR33","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.neunet.2023.12.006","volume":"171","author":"J Yao","year":"2024","unstructured":"Yao, J., et al.: Position-based anchor optimization for point supervised dense nuclei detection. Neural Netw. 171, 159\u2013170 (2024)","journal-title":"Neural Netw."},{"doi-asserted-by":"crossref","unstructured":"Zheng, S., et al.: Conditional random fields as recurrent neural networks, pp. 1529\u20131537 (2015)","key":"13_CR34","DOI":"10.1109\/ICCV.2015.179"},{"doi-asserted-by":"publisher","unstructured":"Zhou, Y., et al.: Prior-aware neural network for partially-supervised multi-organ segmentation. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 10671\u201310680 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.01077","key":"13_CR35","DOI":"10.1109\/ICCV.2019.01077"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78201-5_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T16:03:11Z","timestamp":1733068991000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78201-5_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"ISBN":["9783031782008","9783031782015"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78201-5_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"2 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 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":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}