  Recently, large vision model, Segment Anything Model (SAM), has
revolutionized the computer vision field, especially for image segmentation.
SAM presented a new promptable segmentation paradigm that exhibit its
remarkable zero-shot generalization ability. An extensive researches have
explore the potential and limits of SAM in various downstream tasks. In this
study, we presents $\mathrm{SAM^{Med}}$, an enhanced framework for medical
image annotation that leverages the capabilities of SAM. $\mathrm{SAM^{Med}}$
framework consisted of two submodules, namely $\mathrm{SAM^{assist}}$ and
$\mathrm{SAM^{auto}}$. The $\mathrm{SAM^{assist}}$ demonstrates the
generalization ability of SAM to the downstream medical segmentation task using
the prompt-learning approach. Results show a significant improvement in
segmentation accuracy with only approximately 5 input points. The
$\mathrm{SAM^{auto}}$ model aims to accelerate the annotation process by
automatically generating input prompts. The proposed SAP-Net model achieves
superior segmentation performance with only five annotated slices, achieving an
average Dice coefficient of 0.80 and 0.82 for kidney and liver segmentation,
respectively. Overall, $\mathrm{SAM^{Med}}$ demonstrates promising results in
medical image annotation. These findings highlight the potential of leveraging
large-scale vision models in medical image annotation tasks.
