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SAM3D: Segment Anything Model in Volumetric Medical Images

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arxiv 2309.03493 v4 pith:KRJ3ODBU submitted 2023-09-07 eess.IV cs.CV

Nhat-Tan Bui , Dinh-Hieu Hoang , Minh-Triet Tran , Gianfranco Doretto , Donald Adjeroh , Brijesh Patel , Arabinda Choudhary , Ngan Le This is my paper · ORCID
classification eess.IVcs.CV
keywords imagemedicalmodelsam3danalysismethodssegmentsegmentation
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

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  1. RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2

    cs.CV 2025-02 conditional novelty 5.0 of 10

    RFMedSAM 2, a SAM 2 variant with adapters and a U-Net prompt generator, reports state-of-the-art Dice scores on AMOS2022 (90.7%) and BTCV (86.7%).

  2. Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation

    cs.CV 2025-02 conditional novelty 3.0 of 10

    Self-Prompt-SAM automatically generates point, box, and mask prompts for a fine-tuned SAM and reports state-of-the-art Dice scores on three medical segmentation benchmarks.

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