SAM3D: Segment Anything Model in Volumetric Medical Images
This work addresses the need for efficient and accurate 3D medical image segmentation, which is incremental as it adapts an existing 2D model to 3D data.
The paper tackles the problem of segmenting 3D volumetric medical images by adapting the Segment Anything Model (SAM) to process entire volumes directly, achieving competitive results with state-of-the-art methods while being more parameter-efficient.
Image segmentation remains a pivotal component in medical image analysis, aiding in the extraction of critical information for precise diagnostic practices. With the advent of deep learning, automated image segmentation methods have risen to prominence, showcasing exceptional proficiency in processing medical imagery. Motivated by the Segment Anything Model (SAM)-a foundational model renowned for its remarkable precision and robust generalization capabilities in segmenting 2D natural images-we introduce SAM3D, an innovative adaptation tailored for 3D volumetric medical image analysis. Unlike current SAM-based methods that segment volumetric data by converting the volume into separate 2D slices for individual analysis, our SAM3D model processes the entire 3D volume image in a unified approach. Extensive experiments are conducted on multiple medical image datasets to demonstrate that our network attains competitive results compared with other state-of-the-art methods in 3D medical segmentation tasks while being significantly efficient in terms of parameters. Code and checkpoints are available at https://github.com/UARK-AICV/SAM3D.