Unsupervised Multi-scale Segmentation of Cellular Cryo-electron Tomograms with Stable Diffusion Foundation Model

Uddin, M. R.; Nguyen, T.-H.; Tabib, H. M. S.; Gandhi, K.; Xu, M.

doi:10.1101/2025.06.25.661425

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bioRxiv · 10.1101/2025.06.25.661425

Unsupervised Multi-scale Segmentation of Cellular Cryo-electron Tomograms with Stable Diffusion Foundation Model

Uddin, M. R.·Nguyen, T.-H.·Tabib, H. M. S.·Gandhi, K.·Xu, M.

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Abstract

We introduce an unsupervised approach for segmenting multiscale subcellular objects in 3D volumetric cryo-electron tomography (cryo-ET) images, addressing key challenges such as large data volumes, low signal-to-noise ratios, and the heterogeneity of subcellular shapes and sizes. The method requires users to select a small number of slabs from a few representative tomograms in the dataset. It leverages features extracted from all layers of a Stable Diffusion foundation model, followed by a novel heuristic-based feature aggregation strategy. Segmentation masks are generated using adaptive thresholding, refined with CellPose to split composite regions, and then utilized as pseudo-ground truth for training deep learning models. We validated our pipeline on publicly available cryo-ET datasets of S. Pombe and C. Eleganscell sections, demonstrating performance that closely approximates expert human annotations. This fully automated, data-driven framework enables the mining of multi-scale subcellular patterns, paving the way for accelerated biological discoveries from large-scale cellular cryo-ET datasets.

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Uddin, M. R., Nguyen, T.-H., Tabib, H. M. S., Gandhi, K., Xu, M.. 2025-06-28. Unsupervised Multi-scale Segmentation of Cellular Cryo-electron Tomograms with Stable Diffusion Foundation Model. https://doi.org/10.1101/2025.06.25.661425

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