August 14, 2025Deleted JournalOpen Access

RefSAM3D: Adapting the Segment Anything Model with cross-modal references for three-dimensional medical image segmentation

View Full Paper

Ask AI

Bookmark

Authors

XGXiang GaoShihezi University KLKai LüNational University of Defense Technology

Discussion

Member takes

Implication

RefSAM3D adapts segment anything model for improved segmentation of complex anatomical structures, suggesting better accuracy in three-dimensional medical imaging.

Key Points

RefSAM3D significantly enhances segmentation accuracy in three-dimensional medical imaging tasks.
The model improves performance on multiple datasets, outperforming previous state-of-the-art methods in 3D imaging.
RefSAM3D incorporates cross-modal references and a hierarchical attention mechanism to integrate spatial information.
This advancement highlights the potential of segment anything model in complex medical scenarios, enabling better organ segmentation.

Cite This Study

Gao et al. (2025) studied this question.

synapsesocial.com/papers/68af453aad7bf08b1ead2a5f https://doi.org/10.36922/aih025080010

View Full Paper

Ask AI

Bookmark

Implication

RefSAM3D adapts segment anything model for improved segmentation of complex anatomical structures, suggesting better accuracy in three-dimensional medical imaging.

RefSAM3D: Adapting the Segment Anything Model with cross-modal references for three-dimensional medical image segmentation

Discussion

Member takes

Implication

Key Points

Cite This Study

RefSAM3D: Adapting the Segment Anything Model with cross-modal references for three-dimensional medical image segmentation

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider