  Background: The segment-anything model (SAM), introduced in April 2023, shows
promise as a benchmark model and a universal solution to segment various
natural images. It comes without previously-required re-training or fine-tuning
specific to each new dataset.
  Purpose: To test SAM's accuracy in various medical image segmentation tasks
and investigate potential factors that may affect its accuracy in medical
images.
  Methods: SAM was tested on 12 public medical image segmentation datasets
involving 7,451 subjects. The accuracy was measured by the Dice overlap between
the algorithm-segmented and ground-truth masks. SAM was compared with five
state-of-the-art algorithms specifically designed for medical image
segmentation tasks. Associations of SAM's accuracy with six factors were
computed, independently and jointly, including segmentation difficulties as
measured by segmentation ability score and by Dice overlap in U-Net, image
dimension, size of the target region, image modality, and contrast.
  Results: The Dice overlaps from SAM were significantly lower than the five
medical-image-based algorithms in all 12 medical image segmentation datasets,
by a margin of 0.1-0.5 and even 0.6-0.7 Dice. SAM-Semantic was significantly
associated with medical image segmentation difficulty and the image modality,
and SAM-Point and SAM-Box were significantly associated with image segmentation
difficulty, image dimension, target region size, and target-vs-background
contrast. All these 3 variations of SAM were more accurate in 2D medical
images, larger target region sizes, easier cases with a higher Segmentation
Ability score and higher U-Net Dice, and higher foreground-background contrast.
