| Autor: |
Zhao Y; Carnegie Mellon University, Pittsburgh PA 15213, USA., Bian H; Carnegie Mellon University, Pittsburgh PA 15213, USA., Mu M; Carnegie Mellon University, Pittsburgh PA 15213, USA., Uddin MR; Carnegie Mellon University, Pittsburgh PA 15213, USA., Li Z; University of Alabama at Birmingham, Birmingham AL 35294, USA., Li X; Carnegie Mellon University, Pittsburgh PA 15213, USA., Wang T; University of Alabama at Birmingham, Birmingham AL 35294, USA., Xu M; Carnegie Mellon University, Pittsburgh PA 15213, USA. |
| Abstrakt: |
Cryogenic Electron Tomography (CryoET) is a useful imaging technology in structural biology that is hindered by its need for manual annotations, especially in particle picking. Recent works have endeavored to remedy this issue with few-shot learning or contrastive learning techniques. However, supervised training is still inevitable for them. We instead choose to leverage the power of existing 2D foundation models and present a novel, training-free framework, CryoSAM. In addition to prompt-based single-particle instance segmentation, our approach can automatically search for similar features, facilitating full tomogram semantic segmentation with only one prompt. CryoSAM is composed of two major parts: 1) a prompt-based 3D segmentation system that uses prompts to complete single-particle instance segmentation recursively with Cross-Plane Self-Prompting, and 2) a Hierarchical Feature Matching mechanism that efficiently matches relevant features with extracted tomogram features. They collaborate to enable the segmentation of all particles of one category with just one particle-specific prompt. Our experiments show that CryoSAM outperforms existing works by a significant margin and requires even fewer annotations in particle picking. Further visualizations demonstrate its ability when dealing with full tomogram segmentation for various subcellular structures. Our code is available at: https://github.com/xulabs/aitom. |
