Segment anything model for medical image segmentation: Current applications and future directions

• Published in: Computers in biology and medicine Vol. 171; p. 108238
• Main Authors: Zhang, Yichi, Shen, Zhenrong, Jiao, Rushi
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2024; Elsevier Limited
• Subjects: Benchmarking; Biology; Computer vision; Computers; Empirical analysis; Foundation models; Image analysis; Image processing; Image Processing, Computer-Assisted; Image segmentation; Internal Medicine; Language; Medical image segmentation; Medical imaging; Medicine; Natural Language Processing; Other; S-Adenosylmethionine; Segment anything model; Survey; Uncertainty; Survey; Foundation models; Segment anything model; Medical image segmentation

Due to the inherent flexibility of prompting, foundation models have emerged as the predominant force in the fields of natural language processing and computer vision. The recent introduction of the Segment Anything Model (SAM) signifies a noteworthy expansion of the prompt-driven paradigm into the...