  The Segmentation Anything Model (SAM) has gained significant attention for
its robust generalization capabilities across diverse downstream tasks.
However, the performance of SAM is noticeably diminished in medical images due
to the substantial disparity between natural and medical image domain. In this
paper, we present a zero-shot generalization model specifically designed for
echocardiography analysis, called MediViSTA-SAM. Our key components include (i)
the introduction of frame-level self-attention, which leverages cross-frame
attention across each frame and its neighboring frames to guarantee consistent
segmentation outcomes, and (ii) we utilize CNN backbone for feature embedding
for the subsequent Transformer for efficient fine-tuning while keeping most of
the SAM's parameter reusable. Experiments were conducted using zero-shot
segmentation on multi-vendor in-house echocardiography datasets, indicating
evaluation without prior exposure to the in-house dataset during training.
MediViSTA-SAM effectively overcomes SAM's limitations and can be deployed
across various hospital settings without the necessity of re-training models on
their respective datasets. Our code is open sourced at:
\url{https://github.com/kimsekeun/MediViSTA-SAM}
