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The segment anything model (SAM) was released as a foundation model for imagesegmentation. The promptable segmentation model was trained by over 1 billionmasks on 11M licensed and privacy-respecting images. The model supportszero-shot image segmentation with various segmentation prompts (e.g., points,boxes, masks). It makes the SAM attractive for medical image analysis,especially for digital pathology where the training data are rare. In thisstudy, we evaluate the zero-shot segmentation performance of SAM model onrepresentative segmentation tasks on whole slide imaging (WSI), including (1)tumor segmentation, (2) non-tumor tissue segmentation, (3) cell nucleisegmentation. Core Results: The results suggest that the zero-shot SAM modelachieves remarkable segmentation performance for large connected objects.However, it does not consistently achieve satisfying performance for denseinstance object segmentation, even with 20 prompts (clicks/boxes) on eachimage. We also summarized the identified limitations for digital pathology: (1)image resolution, (2) multiple scales, (3) prompt selection, and (4) modelfine-tuning. In the future, the few-shot fine-tuning with images fromdownstream pathological segmentation tasks might help the model to achievebetter performance in dense object segmentation.

Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging