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Recently, large vision model, Segment Anything Model (SAM), hasrevolutionized the computer vision field, especially for image segmentation.SAM presented a new promptable segmentation paradigm that exhibit itsremarkable zero-shot generalization ability. An extensive researches haveexplore the potential and limits of SAM in various downstream tasks. In thisstudy, we presents $\mathrm{SAM^{Med}}$, an enhanced framework for medicalimage 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 thegeneralization ability of SAM to the downstream medical segmentation task usingthe prompt-learning approach. Results show a significant improvement insegmentation accuracy with only approximately 5 input points. The$\mathrm{SAM^{auto}}$ model aims to accelerate the annotation process byautomatically generating input prompts. The proposed SAP-Net model achievessuperior segmentation performance with only five annotated slices, achieving anaverage Dice coefficient of 0.80 and 0.82 for kidney and liver segmentation,respectively. Overall, $\mathrm{SAM^{Med}}$ demonstrates promising results inmedical image annotation. These findings highlight the potential of leveraginglarge-scale vision models in medical image annotation tasks.

$\mathrm{SAM^{Med}}$: A medical image annotation framework based on large vision model