Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
331-02-009 / 331-02-009 ISMRM Abstract

UnA2LGENet: A Generalizable SAM-Adaptor for Multicenter LGE of Myocardial Infarction Across 1000+ patients

Primary: Cardiovascular - Myocardium
Secondary: Analysis Methods - Segmentation and Detection
331-02-009 · AI Frontiers in Image Analysis · Monday, 11 May, 1:50 PM–3:06 PM · Roof Terrace
331-02-009 · AI Frontiers in Image Analysis · Monday, 11 May, 1:50 PM–3:06 PM · Roof Terrace
Keywords: Cardiac MRI Myocardial infarction Deep Learning Segmentation Multicentric studies
Accepted
Xiuzheng Yue 1, Jing Qi1, Miao Hu1,2, Yinyin Chen3,4, Hang Jin3,4, Tao Li5, Kunlun He1
1Medical Big Data Research Center, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China
2Chinese PLA Medical School, Chinese PLA General Hospital, Beijing, China
3Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
4Department of Medical Imaging, Shanghai Medical School, Fudan University, Shanghai, China
5The First Medical Center, Chinese PLA General Hospital, Beijing, China
Presenting Author: Xiuzheng Yue

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References

1. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203-211.
2. Ma J, He Y, Li F, Han L, You C, Wang B, et al. Segment anything in medical images (MedSAM). Nat Commun. 2024;15:654.
3. Lalande A, Chen Z, Decourselle T, Qayyum A, Pommier T, Lorgis L, et al. EMIDEC: A database usable for the automatic evaluation of myocardial infarction from delayed-enhancement cardiac MRI. Data. 2020;5(4):89.
4. MYOSAIQ Challenge. CREATIS/INSA-Lyon; dataset and challenge description (multicenter LGE-CMR). Accessed: October 29, 2025.
5. Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, et al. Segment Anything. arXiv preprint arXiv:2304.02643. 2023.
6. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. In: MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Cham: Springer; 2015:234-241.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/331-02-009