SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model. (arXiv:2304.05396v1 [eess.IV])

Foundation models have taken over natural language processing and image
generation domains due to the flexibility of prompting. With the recent
introduction of the Segment Anything Model (SAM), this prompt-driven paradigm
has entered image segmentation with a hitherto unexplored abundance of
capabilities. The purpose of this paper is to conduct an initial evaluation of
the out-of-the-box zero-shot capabilities of SAM for medical image
segmentation, by evaluating its performance on an abdominal CT organ
segmentation task, via point or bounding box based prompting. We show that SAM
generalizes well to CT data, making it a potential catalyst for the advancement
of semi-automatic segmentation tools for clinicians. We believe that this
foundation model, while not reaching state-of-the-art segmentation performance
in our investigations, can serve as a highly potent starting point for further
adaptations of such models to the intricacies of the medical domain. Keywords:
medical image segmentation, SAM, foundation models, zero-shot learning



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