Medical SAM for LGE-MRI cardiac segmentation: promise or hype?

In this study, we evaluate MedSAM and SamMed2D for segmenting the left ventricle and myocardium in LGE MR images from a private dataset comprising 135 patients. We first demonstrate that zero-shot performance remains limited, due to the scarcity of LGE MR data in their pretraining. Next, we show that fine-tuning the MedSAM decoder significantly improves segmentation quality, surpassing the nnU-Net baseline, though it requires precise bounding box initialization. We thus propose a modified MedSAM architecture that enables multi-class segmentation from a single bounding box. However, our experiments reveal that despite various improvements, MedSAM continues to produce mixed results. While our approach can segment multiple structures with one bounding box, it still requires accurate initialization, and its performance converges towards that achieved by nnU-Net.