Related Experiment Video
Updated: Apr 19, 2026
09:20
Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
Published on: February 13, 2021
7.2K
Generalization of Left Ventricular Segmentation Models to LVNC Patients: A Comparative Study.
Wenyuan Huang1,2, Lixue Qin1, Lang Hong3,4
1State Key Laboratory of Biomedical Imaging Science and System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, 518055, Shenzhen, China.
Journal of Imaging Informatics in Medicine
|April 17, 2026
Summary
State-of-the-art deep learning models can generalize to rare Left Ventricular Non-compaction (LVNC) cases when trained on diverse cardiac datasets. Model performance depends on training data size and pathology balance, with U-Mamba-Bot showing robustness in limited data scenarios.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Medicine
- Cardiology
Background:
- Left Ventricular Non-compaction (LVNC) is a rare cardiomyopathy with limited representation in public cardiac datasets.
- This scarcity impedes the development of specialized deep learning segmentation models for LVNC.
Purpose of the Study:
- To evaluate the generalization capability of state-of-the-art deep learning models on an LVNC cohort.
- To investigate the impact of training data size and pathology composition on model performance for rare diseases.
Main Methods:
- Benchmarked CNN-based, Transformer-based, Mamba-based, and pre-trained foundation models.
- Trained models on the M&M dataset and tested on an independent LVNC dataset.
- Evaluated performance using Dice score, mIoU, ASD, 95% HD, and EF estimation error.
Main Results:
- STU-Net, nnU-Net, and U-Mamba-Bot demonstrated strong generalization to LVNC.
- Achieved LV Dice scores up to 91%, MYO Dice scores up to 80%, and EF estimation errors as low as 4.6% MAE.
- U-Mamba-Bot showed superior robustness with limited data and unbalanced pathologies; diverse training data generally improved performance, but excess HCM cases reduced accuracy.
Conclusions:
- State-of-the-art segmentation models can generalize to rare diseases like LVNC with sufficient multi-pathology training data.
- Careful selection and balancing of pathology types are crucial for robust model performance in data-scarce, rare disease scenarios.
