  The Segment Anything Model (SAM) made an eye-catching debut recently and
inspired many researchers to explore its potential and limitation in terms of
zero-shot generalization capability. As the first promptable foundation model
for segmentation tasks, it was trained on a large dataset with an unprecedented
number of images and annotations. This large-scale dataset and its promptable
nature endow the model with strong zero-shot generalization. Although the SAM
has shown competitive performance on several datasets, we still want to
investigate its zero-shot generalization on medical images. As we know, the
acquisition of medical image annotation usually requires a lot of effort from
professional practitioners. Therefore, if there exists a foundation model that
can give high-quality mask prediction simply based on a few point prompts, this
model will undoubtedly become the game changer for medical image analysis. To
evaluate whether SAM has the potential to become the foundation model for
medical image segmentation tasks, we collected more than 12 public medical
image datasets that cover various organs and modalities. We also explore what
kind of prompt can lead to the best zero-shot performance with different
modalities. Furthermore, we find that a pattern shows that the perturbation of
the box size will significantly change the prediction accuracy. Finally,
Extensive experiments show that the predicted mask quality varied a lot among
different datasets. And providing proper prompts, such as bounding boxes, to
the SAM will significantly increase its performance.
