  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.
