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Image segmentation remains a pivotal component in medical image analysis,aiding in the extraction of critical information for precise diagnosticpractices. With the advent of deep learning, automated image segmentationmethods have risen to prominence, showcasing exceptional proficiency inprocessing medical imagery. Motivated by the Segment Anything Model (SAM)-afoundational model renowned for its remarkable precision and robustgeneralization 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 thevolume into separate 2D slices for individual analysis, our SAM3D modelprocesses the entire 3D volume image in a unified approach. Extensiveexperiments are conducted on multiple medical image datasets to demonstratethat our network attains competitive results compared with otherstate-of-the-art methods in 3D medical segmentation tasks while beingsignificantly efficient in terms of parameters. Code and checkpoints areavailable at https://github.com/UARK-AICV/SAM3D.

SAM3D: Segment Anything Model in Volumetric Medical Images