Deep Learning Myocardial Segmentation in 3D Whole-Heart Joint T1/T2 mapping: Comparison of nnU-Net and MA-SAM

Motivation: The significant amount of data collected from a single 3D whole-heart joint T1/T2 mapping sequence substantially increases the time required for segmenting and analyzing the quantitative maps, therefore, automating the segmentation process could result in a significant reduction. Goal(s): To automate the segmentation of myocardium using state-of-the-art segmentation networks. Approach: Two segmentation networks, nnUNet and MA-SAM, are trained and compared for myocardial segmentation of whole-heart joint T1/T2 mapping in healthy subjects and patients. Results: nnUNET and MA-SAM achieved good quality segmentations with DICE score higher than ~0.856 with smoothed masks. nnU-Net achieved better results in term DICE and required the shortest training time. Impact: State-of-the-art nnUNET and MA-SAM networks achieve accurate automatic myocardial segmentation of whole-heart joint T1/T2 mapping. This can significantly reduce the laborious task of manual segmentation and could help to accelerate the analysis and therefore the diagnosis of myocardium-related disease.