Segment anything model for medical image analysis: An experimental study.

ABSTRACT: Training segmentation models for medical images continues to be challenging due to the limited availability of data annotations. Segment Anything Model (SAM) is a foundation model trained on over 1 billion annotations, predominantly for natural images, that is intended to segment user-defined objects of interest in an interactive manner. While the model performance on natural images is impressive, medical image domains pose their own set of challenges. Here, we perform an extensive evaluation of SAM's ability to segment medical images on a collection of 19 medical imaging datasets from various modalities and anatomies. In our experiments, we generated point and box prompts for SAM using a standard method that simulates interactive segmentation. We report the following findings: (1) SAM's p

SUBMITTER: Mazurowski MA 

PROVIDER: S-EPMC10528428 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature