AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs
This work addresses domain generalization for atrial fibrillation treatment planning, but it is incremental as it compares existing methods without introducing new ones.
The paper tackled the problem of poor generalization in left atrial segmentation from multi-center LGE MRIs by evaluating four segmentation networks and three domain generalization strategies, finding that simple histogram matching was most effective.
Left atrial (LA) segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is a crucial step needed for planning the treatment of atrial fibrillation. However, automatic LA segmentation from LGE MRI is still challenging, due to the poor image quality, high variability in LA shapes, and unclear LA boundary. Though deep learning-based methods can provide promising LA segmentation results, they often generalize poorly to unseen domains, such as data from different scanners and/or sites. In this work, we collect 210 LGE MRIs from different centers with different levels of image quality. To evaluate the domain generalization ability of models on the LA segmentation task, we employ four commonly used semantic segmentation networks for the LA segmentation from multi-center LGE MRIs. Besides, we investigate three domain generalization strategies, i.e., histogram matching, mutual information based disentangled representation, and random style transfer, where a simple histogram matching is proved to be most effective.