The Segment Anything Model (SAM), originally built on a two-dimensional vision transformer, excels at capturing global patterns in two-dimensional natural images but faces challenges when applied to three-dimensional (3D) medical imaging modalities such as computed tomography and magnetic resonance imaging. These modalities require capturing spatial information in volumetric space for tasks such as organ segmentation and tumor quantification. To address this challenge, we introduce RefSAM3D, an adaptation of SAM for 3D medical imaging by incorporating a 3D image adapter and cross-modal reference prompt generation. Our approach modifies the visual encoder to handle 3D inputs and enhances the mask decoder for direct 3D mask generation. We also integrate textual prompts to improve segmentation accuracy and consistency in complex anatomical scenarios. By employing a hierarchical attention mechanism, our model effectively captures and integrates information across different scales. Extensive evaluations on multiple medical imaging datasets demonstrate that RefSAM3D outperforms state-of-the-art methods. Our work thus advances the application of SAM in accurately segmenting complex anatomical structures in medical imaging.
Gao et al. (Thu,) studied this question.
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