Training-free CryoET Tomogram Segmentation
arxiv(2024)
摘要
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
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