Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation
This addresses a domain-specific issue for medical imaging researchers and clinicians by enhancing cardiac diagnosis through more accurate segmentation, though it appears incremental as it builds on existing deep learning methods.
The paper tackles the problem of poor segmentation of irregularly shaped organs like the right ventricle in cardiac MR images by proposing a Multi-Disease-Aware Training Strategy, which improved segmentation performance, especially for the right ventricle, and showed robustness on unknown disease data.
Accurate segmentation of the ventricles from cardiac magnetic resonance images (CMRIs) is crucial for enhancing the diagnosis and analysis of heart conditions. Deep learning-based segmentation methods have recently garnered significant attention due to their impressive performance. However, these segmentation methods are typically good at partitioning regularly shaped organs, such as the left ventricle (LV) and the myocardium (MYO), whereas they perform poorly on irregularly shaped organs, such as the right ventricle (RV). In this study, we argue that this limitation of segmentation models stems from their insufficient generalization ability to address the distribution shift of segmentation targets across slices, cardiac phases, and disease conditions. To overcome this issue, we present a Multi-Disease-Aware Training Strategy (MTS) and restructure the introduced CMRI datasets into multi-disease datasets. Additionally, we propose a specialized data processing technique for preprocessing input images to support the MTS. To validate the effectiveness of our method, we performed control group experiments and cross-validation tests. The experimental results show that (1) network models trained using our proposed strategy achieved superior segmentation performance, particularly in RV segmentation, and (2) these networks exhibited robust performance even when applied to data from unknown diseases.