{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T06:03:12Z","timestamp":1772690592526,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,28]],"date-time":"2025-12-28T00:00:00Z","timestamp":1766880000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T00:00:00Z","timestamp":1767398400000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"National Administration of Traditional Chinese Medicine Research Project","award":["No. 0102023703"],"award-info":[{"award-number":["No. 0102023703"]}]},{"name":"Project of the State Key Laboratory of Dampness Syndrome of Traditional Chinese Medicine jointly established by the province and the ministry","award":["No.SZ2022KF10"],"award-info":[{"award-number":["No.SZ2022KF10"]}]},{"name":"Scientific Research Initiation Project of Guangdong Provincial Hospital of Traditional Chinese Medicine","award":["No.2021KT1709"],"award-info":[{"award-number":["No.2021KT1709"]}]},{"name":"Research Project of Guangdong Provincial Bureau of Traditional Chinese Medicine","award":["No.20241120"],"award-info":[{"award-number":["No.20241120"]}]},{"name":"Excellent Young Talents Program of Guangdong Provincial Hospital of Traditional Chinese Medicine","award":["No. SZ2024QN05"],"award-info":[{"award-number":["No. SZ2024QN05"]}]},{"name":"Basic Clinical Collaborative Innovation Program of Guangdong Provincial Hospital of Traditional Chinese Medicine and School of Biomedical Sciences, The Chinese University of Hong Kong","award":["No. YN2024HK01"],"award-info":[{"award-number":["No. YN2024HK01"]}]},{"name":"Guangxi Natural Science Foundation","award":["Grant Nos. 2025GXNSFAA069056 and AB24010153"],"award-info":[{"award-number":["Grant Nos. 2025GXNSFAA069056 and AB24010153"]}]},{"name":"Guangzhou Science and Technology Bureau Project","award":["No.2024A03J1165"],"award-info":[{"award-number":["No.2024A03J1165"]}]},{"name":"The Basic and Applied Basic Research Project of Guangzhou Municiple Science and Technology Bureau","award":["No.2025A03J4265"],"award-info":[{"award-number":["No.2025A03J4265"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["NO.82374240"],"award-info":[{"award-number":["NO.82374240"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Guangdong Province Basic and Applied Basic Research Fund Project","award":["No. 2025A1515012690"],"award-info":[{"award-number":["No. 2025A1515012690"]}]},{"name":"Guangdong Provincial Key Laboratory of Research on Emergency in TCM","award":["No. 2023B1212060062"],"award-info":[{"award-number":["No. 2023B1212060062"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-025-02163-3","type":"journal-article","created":{"date-parts":[[2025,12,28]],"date-time":"2025-12-28T06:35:23Z","timestamp":1766903723000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["ScarElastic: continuous elasticity field modeling for myocardial scar delineation in LGE-CMR"],"prefix":"10.1038","volume":"9","author":[{"given":"Rongjun","family":"Zou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yugui","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuechun","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanmou","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donghao","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyu","family":"Bo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siya","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinhai","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoping","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,28]]},"reference":[{"key":"2163_CR1","first-page":"33","volume":"35","author":"RJ Kim","year":"2000","unstructured":"Kim, R. J. et al. The relationship of left ventricular mass to infarct size and left ventricular ejection fraction after acute myocardial infarction. J. Am. Coll. Cardiol. 35, 33\u201341 (2000).","journal-title":"J. Am. Coll. Cardiol."},{"key":"2163_CR2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1532-429X-15-1","volume":"15","author":"CM Kramer","year":"2013","unstructured":"Kramer, C. M., Barkhausen, J., Flamm, S. D., Kim, R. J. & Nagel, E. Standardized cardiovascular magnetic resonance imaging (CMR) protocols, society for cardiovascular magnetic resonance: board of trustees task force on standardized protocols. J. Cardiovasc. Magn. Reson. 15, 1\u201312 (2013).","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"2163_CR3","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1001\/jama.2013.1363","volume":"309","author":"A Gulati","year":"2013","unstructured":"Gulati, A. et al. Association of fibrosis with mortality and sudden cardiac death in patients with nonischemic dilated cardiomyopathy. JAMA 309, 896\u2013908 (2013).","journal-title":"JAMA"},{"key":"2163_CR4","first-page":"548","volume":"4","author":"M Disertori","year":"2011","unstructured":"Disertori, M. et al. Myocardial fibrosis assessment by lge is a powerful predictor of ventricular tachyarrhythmias in ischemic and nonischemic cardiomyopathy: a meta-analysis. Circ. Cardiovasc. Imaging 4, 548\u2013556 (2011).","journal-title":"Circ. Cardiovasc. Imaging"},{"key":"2163_CR5","first-page":"3474","volume":"41","author":"C Li","year":"2025","unstructured":"Li, C., Weng, X., Li, Y. & Zhang, T. Multimodal learning engagement assessment system: an innovative approach to optimizing learning engagement. Int. J. Hum. Comput. Interact. 41, 3474\u20133490 (2025).","journal-title":"Int. J. Hum. Comput. Interact."},{"key":"2163_CR6","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1016\/j.jacc.2010.08.649","volume":"57","author":"AS Flett","year":"2011","unstructured":"Flett, A. S. et al. Evaluation of techniques for the quantification of myocardial scar of differing etiology using cardiac magnetic resonance. J. Am. Coll. Cardiol. 57, 986\u2013996 (2011).","journal-title":"J. Am. Coll. Cardiol."},{"key":"2163_CR7","first-page":"219","volume":"57","author":"F Zabihollahy","year":"2020","unstructured":"Zabihollahy, F., Ukwatta, E., Rajchl, M., White, J. A. & Ukwatta, T. Automated segmentation of left atrial fibrosis and scars from late gadolinium enhancement MR images using fully convolutional networks. Med. Image Anal. 57, 219\u2013229 (2020).","journal-title":"Med. Image Anal."},{"key":"2163_CR8","first-page":"863716","volume":"9","author":"J Duan","year":"2022","unstructured":"Duan, J. et al. Automatic scar segmentation in lge MRI using deep learning: state-of-the-art and open challenges. Front. Cardiovasc. Med. 9, 863716 (2022).","journal-title":"Front. Cardiovasc. Med."},{"key":"2163_CR9","first-page":"1452","volume":"107","author":"P Wohlfahrt","year":"2021","unstructured":"Wohlfahrt, P. et al. Scar quantification by cardiovascular magnetic resonance: state-of-the-art and future directions. Heart 107, 1452\u20131459 (2021).","journal-title":"Heart"},{"key":"2163_CR10","doi-asserted-by":"publisher","unstructured":"Li, X. et al. Claim: cardiac late gadolinium enhancement image augmentation via diffusion models and anatomical priors. arXiv https:\/\/doi.org\/10.48550\/arXiv.2506.15549 (2025).","DOI":"10.48550\/arXiv.2506.15549"},{"key":"2163_CR11","doi-asserted-by":"publisher","unstructured":"Wang, W. et al. Scarnet: foundation model for myocardial scar segmentation in LGE-MRI. arXivhttps:\/\/doi.org\/10.48550\/arXiv.2501.01372 (2025).","DOI":"10.48550\/arXiv.2501.01372"},{"key":"2163_CR12","first-page":"834","volume":"110","author":"LC Amado","year":"2004","unstructured":"Amado, L. C. et al. Accurate and objective infarct sizing by contrast-enhanced magnetic resonance imaging in a canine myocardial infarction model. Circulation 110, 834\u2013840 (2004).","journal-title":"Circulation"},{"key":"2163_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/10447318.2025.2546039","volume":"0","author":"J Zhong","year":"2025","unstructured":"Zhong, J., Fang, X., Yang, Z., Tian, Z. & Li, C. Skybound magic: enabling body-only drone piloting through a lightweight vision-pose interaction framework. Int. J. Hum. Comput. Interact. 0, 1\u201331 (2025).","journal-title":"Int. J. Hum. Comput. Interact."},{"key":"2163_CR14","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1111\/jce.12651","volume":"26","author":"H Cochet","year":"2015","unstructured":"Cochet, H. et al. Scar extent and location: quantitative evaluation by LGE-MRI of the left atrium in patients with atrial fibrillation. J. Cardiovasc. Electrophysiol. 26, 610\u2013618 (2015).","journal-title":"J. Cardiovasc. Electrophysiol."},{"key":"2163_CR15","unstructured":"Zhuang, X., Rhode, K., Razavi, R., Hawkes, D. J. & Ourselin, S. Registration-based propagation for whole heart segmentation on CT images. in Medical Image Computing and Computer-Assisted Intervention (MICCAI), 425\u2013432 (Springer, 2010)."},{"key":"2163_CR16","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1109\/TIP.2005.863965","volume":"15","author":"C Li","year":"2006","unstructured":"Li, C., Kao, C.-Y., Gore, J. C. & Ding, Z. Active contour models driven by local image fitting energy. IEEE Trans. Image Process. 15, 865\u2013877 (2006).","journal-title":"IEEE Trans. Image Process."},{"key":"2163_CR17","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation 234\u2013241 (2015).","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2163_CR18","doi-asserted-by":"publisher","unstructured":"Oktay, O. et al. Attention U-Net: learning where to look for the pancreas. arXiv https:\/\/doi.org\/10.48550\/arXiv.1804.03999 (2018).","DOI":"10.48550\/arXiv.1804.03999"},{"key":"2163_CR19","doi-asserted-by":"publisher","unstructured":"Chen, J. et al. Transunet: transformers make strong encoders for medical image segmentation. arXiv https:\/\/doi.org\/10.48550\/arXiv.2102.04306 (2021).","DOI":"10.48550\/arXiv.2102.04306"},{"key":"2163_CR20","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., Kohl, S., Petersen, J. & Maier-Hein, K. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 203\u2013211 (2021).","journal-title":"Nat. Methods"},{"key":"2163_CR21","first-page":"54","volume":"36","author":"W Bai","year":"2017","unstructured":"Bai, W. et al. Semi-supervised learning for network-based cardiac MR image segmentation. Med. Image Anal. 36, 54\u201363 (2017).","journal-title":"Med. Image Anal."},{"key":"2163_CR22","doi-asserted-by":"publisher","first-page":"101693","DOI":"10.1016\/j.media.2020.101693","volume":"63","author":"N Tajbakhsh","year":"2020","unstructured":"Tajbakhsh, N. et al. Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Med. Image Anal. 63, 101693 (2020).","journal-title":"Med. Image Anal."},{"key":"2163_CR23","unstructured":"Chartsias, A., Joyce, T., Giuffrida, M. V. & Tsaftaris, S. A. Scargan: chained generative adversarial networks to simulate pathological tissue on cardiovascular MR scans. in Simulation and Synthesis in Medical Imaging, 147\u2013157 (Springer, 2018)."},{"key":"2163_CR24","doi-asserted-by":"crossref","unstructured":"Park, J. J., Florence, P., Straub, J., Newcombe, R. & Lovegrove, S. Deepsdf: learning continuous signed distance functions for shape representation. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 165\u2013174 (IEEE\/CVF, 2019).","DOI":"10.1109\/CVPR.2019.00025"},{"key":"2163_CR25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1475-925X-11-1","volume":"11","author":"JD Bayer","year":"2012","unstructured":"Bayer, J. D., Blake, R. C., Plank, G. & Trayanova, N. A. Modeling cardiac fiber orientation in patient-specific geometries: a rule-based approach. Biomed. Eng. OnLine 11, 1\u201321 (2012).","journal-title":"Biomed. Eng. OnLine"},{"key":"2163_CR26","doi-asserted-by":"crossref","unstructured":"Pfeiffer, E. R., Tangney, J. R., Omens, J. H. & McCulloch, A. D. Biomechanics of cardiac electromechanical coupling and mechanoelectric feedback. J. Biomech. Eng. 136, 021007 (2014).","DOI":"10.1115\/1.4026221"},{"key":"2163_CR27","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1109\/TMI.2017.2743464","volume":"37","author":"O Oktay","year":"2017","unstructured":"Oktay, O. et al. Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Trans. Med. Imaging 37, 384\u2013395 (2017).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2163_CR28","doi-asserted-by":"crossref","unstructured":"Mosinska, A., Marquez-Neila, P., Kozinski, M. & Fua, P. Beyond the pixel-wise loss for topology-aware delineation. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3136\u20133145 (IEEE\/CVF, 2018).","DOI":"10.1109\/CVPR.2018.00331"},{"key":"2163_CR29","unstructured":"Zhuang, X. et al. Cardiac segmentation on late gadolinium enhancement MRI: a benchmark study from the multi-sequence cardiac MR segmentation challenge. in Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, Myocardial Infarction and Scar Segmentation Challenges, 161\u2013170 (Springer, 2020)."},{"key":"2163_CR30","unstructured":"Zhuang, X. et al. Myocardial pathology segmentation combining multi-sequence cardiac magnetic resonance images. in Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, Myocardial Infarction and Scar Segmentation Challenges, 140\u2013151 (Springer, 2021)."},{"key":"2163_CR31","unstructured":"Zhuang, X. et al. MS-CMRSeg challenge: multi-sequence cardiac MR segmentation for fully automated myocardial and scar segmentation. in Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, Myocardial Infarction and Scar Segmentation Challenges, 154\u2013163 (Springer, 2020)."},{"key":"2163_CR32","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. A., Petersen, J. & Maier-Hein, K. H. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 203\u2013211 (2021).","journal-title":"Nat. Methods"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02163-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02163-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02163-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T21:56:54Z","timestamp":1767477414000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02163-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,28]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2163"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-02163-3","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,28]]},"assertion":[{"value":"20 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable. This work exclusively utilizes de-identified datasets available from public repositories.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"3"}}