SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation

Understand

The Segment Anything Model (SAM) exhibits a capability to segment a wide array of objects in natural images, serving as a versatile perceptual tool for various downstream image segmentation tasks.

• In contrast, medical image segmentation tasks often rely on domain-specific knowledge (DSK).
• In this paper, we propose a novel method that combines the segmentation foundation model (i.e., SAM) with domain-specific knowledge for reliable utilization of unlabeled images in building a medical image segmentation model.
• Our new method is iterative and consists of two main stages: (1) segmentation model training; (2) expanding the labeled set by using the trained segmentation model, an unlabeled set, SAM, and domain-specific knowledge.