{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T08:09:47Z","timestamp":1771056587872,"version":"3.50.1"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031723773","type":"print"},{"value":"9783031723780","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\/978-3-031-72378-0_25","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T07:02:53Z","timestamp":1727852573000},"page":"264-274","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Generating Anatomically Accurate Heart Structures via\u00a0Neural Implicit Fields"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4455-7145","authenticated-orcid":false,"given":"Jiancheng","family":"Yang","sequence":"first","affiliation":[]},{"given":"Ekaterina","family":"Sedykh","sequence":"additional","affiliation":[]},{"given":"Jason Ken","family":"Adhinarta","sequence":"additional","affiliation":[]},{"given":"Hieu","family":"Le","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6702-9970","authenticated-orcid":false,"given":"Pascal","family":"Fua","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"25_CR1","unstructured":"Bengio, Y., L\u00e9onard, N., Courville, A.: Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv Preprint (2013)"},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00609"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Deng, Y., Yang, J., Tong, X.: Deformed implicit field: modeling 3D shapes with learned dense correspondence. In: Conference on Computer Vision and Pattern Recognition, pp. 10286\u201310296 (2021)","DOI":"10.1109\/CVPR46437.2021.01015"},{"key":"25_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102889","volume":"89","author":"S Gao","year":"2023","unstructured":"Gao, S., Zhou, H., Gao, Y., Zhuang, X.: BayeSeg: Bayesian modeling for medical image segmentation with interpretable generalizability. Med. Image Anal. 89, 102889 (2023)","journal-title":"Med. Image Anal."},{"issue":"3","key":"25_CR5","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","volume":"69","author":"H Guan","year":"2021","unstructured":"Guan, H., Liu, M.: Domain adaptation for medical image analysis: a survey. IEEE Trans. Biomed. Eng. 69(3), 1173\u20131185 (2021)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"25_CR6","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1007\/978-3-031-19818-2_40","volume-title":"European Conference on Computer Vision 2022","author":"S Gupta","year":"2022","unstructured":"Gupta, S., et al.: Learning topological interactions for multi-class medical image segmentation. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13689, pp. 701\u2013718. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19818-2_40"},{"issue":"4","key":"25_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3306346.3322959","volume":"38","author":"R Hanocka","year":"2019","unstructured":"Hanocka, R., Hertz, A., Fish, N., Giryes, R., Fleishman, S., Cohen-Or, D.: MeshCNN: a network with an edge. ACM Trans. Graph. 38(4), 1\u201312 (2019)","journal-title":"ACM Trans. Graph."},{"key":"25_CR8","unstructured":"Huang, Z., et al.: STU-Net: scalable and transferable medical image segmentation models empowered by large-scale supervised pre-training. arXiv Preprint (2023)"},{"issue":"2","key":"25_CR9","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"},{"key":"25_CR10","unstructured":"Le, H., Talabot, N., Yang, J., Fua, P.: Enforcing topological interaction between implicit surfaces via uniform sampling. arXiv Preprint (2023)"},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3D reconstruction in function space. In: Conference on Computer Vision and Pattern Recognition, pp. 4460\u20134470 (2019)","DOI":"10.1109\/CVPR.2019.00459"},{"issue":"12","key":"25_CR12","doi-asserted-by":"publisher","first-page":"5568","DOI":"10.1118\/1.3254077","volume":"36","author":"C Metz","year":"2009","unstructured":"Metz, C., Schaap, M., Weustink, A., Mollet, N., van Walsum, T., Niessen, W.: Coronary centerline extraction from CT coronary angiography images using a minimum cost path approach. Med. Phys. 36(12), 5568\u20135579 (2009)","journal-title":"Med. Phys."},{"key":"25_CR13","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: Conference on Computer Vision and Pattern Recognition, pp. 3136\u20133145 (2018)","DOI":"10.1109\/CVPR.2018.00331"},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Park, J.J., Florence, P., Straub, J., Newcombe, R.A., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. In: Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00025"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Park, K., et al.: Nerfies: deformable neural radiance fields. In: International Conference on Computer Vision, pp. 5865\u20135874 (2021)","DOI":"10.1109\/ICCV48922.2021.00581"},{"key":"25_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/978-3-030-58580-8_31","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Peng","year":"2020","unstructured":"Peng, S., Niemeyer, M., Mescheder, L., Pollefeys, M., Geiger, A.: Convolutional occupancy networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 523\u2013540. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58580-8_31"},{"key":"25_CR17","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Conference on Computer Vision and Pattern Recognition, pp. 652\u2013660 (2017)"},{"issue":"5","key":"25_CR18","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1016\/j.media.2009.06.003","volume":"13","author":"M Schaap","year":"2009","unstructured":"Schaap, M., et al.: Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Med. Image Anal. 13(5), 701\u2013714 (2009)","journal-title":"Med. Image Anal."},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Sun, S., Han, K., Kong, D., Tang, H., Yan, X., Xie, X.: Topology-preserving shape reconstruction and registration via neural diffeomorphic flow. In: Conference on Computer Vision and Pattern Recognition, pp. 20845\u201320855 (2022)","DOI":"10.1109\/CVPR52688.2022.02018"},{"key":"25_CR20","unstructured":"Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A., Hardt, M.: Test-time training with self-supervision for generalization under distribution shifts. In: International Conference on Machine Learning, pp. 9229\u20139248. PMLR (2020)"},{"issue":"7","key":"25_CR21","doi-asserted-by":"publisher","first-page":"1460","DOI":"10.1109\/TMI.2015.2398818","volume":"34","author":"C Tobon-Gomez","year":"2015","unstructured":"Tobon-Gomez, C., et al.: Benchmark for algorithms segmenting the left atrium from 3D CT and MRI datasets. IEEE Trans. Med. Imaging 34(7), 1460\u20131473 (2015)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Tomar, D., Vray, G., Bozorgtabar, B., Thiran, J.P.: TeSLA: test-time self-learning with automatic adversarial augmentation. In: Conference on Computer Vision and Pattern Recognition, pp. 20341\u201320350 (2023)","DOI":"10.1109\/CVPR52729.2023.01948"},{"issue":"5","key":"25_CR23","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230024","volume":"5","author":"J Wasserthal","year":"2023","unstructured":"Wasserthal, J., et al.: TotalSegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol. Artif. Intell. 5(5), e230024 (2023)","journal-title":"Radiol. Artif. Intell."},{"key":"25_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/978-3-030-59719-1_30","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"U Wickramasinghe","year":"2020","unstructured":"Wickramasinghe, U., Remelli, E., Knott, G., Fua, P.: Voxel2Mesh: 3D mesh model generation from volumetric data. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 299\u2013308. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_30"},{"key":"25_CR25","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1007\/978-3-031-16443-9_41","volume-title":"Medical Image Computing and Computer Assisted Intervention","author":"U Wickramasinghe","year":"2022","unstructured":"Wickramasinghe, U., Jensen, P., Shah, M., Yang, J., Fua, P.: Weakly supervised volumetric image segmentation with deformed templates. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13435, pp. 422\u2013432. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16443-9_41"},{"key":"25_CR26","unstructured":"Xie, K., Yang, J., Wei, D., Weng, Z., Fua, P.: Efficient anatomical labeling of pulmonary tree structures via implicit point-graph networks. arXiv Preprint (2023)"},{"key":"25_CR27","doi-asserted-by":"crossref","unstructured":"Yang, J., Wickramasinghe, U., Fua, P.: ImplicitAtlas: learning deformable shape templates in medical imaging. In: Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.01540"},{"key":"25_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/978-3-030-87193-2_58","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"J Yang","year":"2021","unstructured":"Yang, J., Gu, S., Wei, D., Pfister, H., Ni, B.: RibSeg dataset and strong point cloud baselines for rib segmentation from CT scans. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 611\u2013621. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_58"},{"key":"25_CR29","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1007\/978-3-031-16440-8_48","volume-title":"Medical Image Computing and Computer-Assisted Intervention","author":"J Yang","year":"2022","unstructured":"Yang, J., Shi, R., Wickramasinghe, U., Zhu, Q., Ni, B., Fua, P.: Neural annotation refinement: development of a new 3D dataset for adrenal gland analysis. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13434, pp. 503\u2013513. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16440-8_48"},{"key":"25_CR30","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Yu, T., Dai, Q., Liu, Y.: Deep implicit templates for 3D shape representation. In: Conference on Computer Vision and Pattern Recognition, pp. 1429\u20131439 (2021)","DOI":"10.1109\/CVPR46437.2021.00148"},{"issue":"12","key":"25_CR31","doi-asserted-by":"publisher","first-page":"2933","DOI":"10.1109\/TPAMI.2018.2869576","volume":"41","author":"X Zhuang","year":"2018","unstructured":"Zhuang, X.: Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE Trans. Pattern Anal. Mach. Intell. 41(12), 2933\u20132946 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101537","volume":"58","author":"X Zhuang","year":"2019","unstructured":"Zhuang, X., et al.: Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge. Med. Image Anal. 58, 101537 (2019)","journal-title":"Med. Image Anal."}],"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\/978-3-031-72378-0_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T07:31:21Z","timestamp":1771054281000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72378-0_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031723773","9783031723780"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72378-0_25","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":"3 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Jiancheng\u00a0Yang holds equity in Dianei Technology but believes this does\u00a0not constitute a competing interest. Other authors declare that\u00a0they have no conflict of interest.","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"}}]}}