Segment Anything Model (SAM) for Medical Image Segmentation: A Preliminary Review
Leying Zhang, Xiaokang Deng, Lu Yu
Abstract
Medical image segmentation is a critical component in a variety of clinical applications, facilitating accurate diagnosis and treatment planning. The Segment Anything Model (SAM), a deep learning architecture, has emerged as a promising solution to the challenges inherent in medical image segmentation. SAM’s superior zero-shot capability allows it to generalize effectively, even in the absence of task-specific segmentation samples. This unique characteristic broadens its application potential across various medical image modalities. This paper provides an in-depth review of SAM, focusing on its application in medical image segmentation. The review discusses the advantages of deep learning image segmentation over traditional methods, emphasizing the superior accuracy, efficiency, and automation that deep learning models offer. The paper also highlights the applications of SAM across various medical imaging modalities, demonstrating its versatility and adaptability. A taxonomy of SAM approaches in medical image segmentation is presented, categorizing them based on modality, dimension, organ, dataset, prompt, and performance. Despite the promising results of SAM, challenges remain in the field of medical image segmentation. The paper identifies these challenges and suggests potential directions for future research. In conclusion, this review aims to provide a comprehensive understanding of SAM and its potential to revolutionize medical image analysis and contribute to advancements in healthcare.