CryoSAM: Training-free CryoET Tomogram Segmentation with Foundation Models.

ABSTRACT: 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 in

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC12923679 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature