  Segmentation in medical imaging is a critical component for the diagnosis,
monitoring, and treatment of various diseases and medical conditions.
Presently, the medical segmentation landscape is dominated by numerous
specialized deep learning models, each fine-tuned for specific segmentation
tasks and image modalities. The recently-introduced Segment Anything Model
(SAM) employs the ViT neural architecture and harnesses a massive training
dataset to segment nearly any object; however, its suitability to the medical
domain has not yet been investigated. In this study, we explore the zero-shot
performance of SAM in medical imaging by implementing eight distinct prompt
strategies across six datasets from four imaging modalities, including X-ray,
ultrasound, dermatoscopy, and colonoscopy. Our findings reveal that SAM's
zero-shot performance is not only comparable to, but in certain cases,
surpasses the current state-of-the-art. Based on these results, we propose
practical guidelines that require minimal interaction while consistently
yielding robust outcomes across all assessed contexts. The source code, along
with a demonstration of the recommended guidelines, can be accessed at
https://github.com/Malta-Lab/SAM-zero-shot-in-Medical-Imaging.
