SAM-driven MaskNet for Left Ventricle Segmentation on Cine DENSE with Unsupervised Domain Adaptation

Motivation: This study leverages unsupervised domain adaptation (UDA) and the Segment-Anything-Model (SAM) for automated and accurate left ventricle (LV) segmentation of displacement encoding with stimulated echoes (DENSE) MRI, enabling reproducible myocardial strain quantification. Goal(s): Develop a UDA-based framework driven by SAM, to investigate its LV segmentation accuracy and reproducibility in strain analysis using DENSE MRI. Approach: MaskNet leverages UDA to transfer segmentation knowledge from cine steady-state free precession (SSFP) images to DENSE images and SAM to generate robust masks. Strain results using MaskNet-derived contours were validated against manual contours. Results: MaskNet demonstrated accurate LV segmentation and reproducible strain results. Impact: This research advances automated LV segmentation in DENSE MRI by integrating UDA and foundation models, leveraging cine SSFP images and established annotations to improve deep learning model performance despite limited availability of specialized MR images like DENSE.