← 研踪 PaperTrailCVF · CVPR 2026:cvf:cbb60f95ace1391b7784b208df9c7910 · 记录 #1
Unsupervised Multi-Scale Segmentation of 3D Subcellular World with Stable Diffusion Foundation Model
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 02:11
摘要
We introduce an unsupervised approach for segmenting multiscale subcellular objects in 3D volumetric cryo-electron tomography (cryo-ET) images. To this end, we address key challenges such as lack of annotated data, large data volumes, high heterogeneity of subcellular shapes and sizes, and high inter-domain variability of cellular cryo-ET images across different experiments and contexts. Our method requires users to only select a small number of slabs from a few representative tomograms in the dataset. The core of our method is extracting features for the corresponding slabs, leveraging a Stable Diffusion foundation model pretrained on mostly natural images. The feature extraction is followed by a novel heuristic-based feature aggregation strategy, and adaptive thresholding to segment the aggregated features. The resulting masks are refined with pretrained CellPose to split composite regions, and then utilized as pseudo-ground truth for training supervised deep learning models. We validated our unsupervised foundation-model based pipeline on publicly available cryo-ET benchmark datasets, 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.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026 · 最新观测版本
Unsupervised Multi-Scale Segmentation of 3D Subcellular World with Stable Diffusion Foundation Model
We introduce an unsupervised approach for segmenting multiscale subcellular objects in 3D volumetric cryo-electron tomography (cryo-ET) images. To this end, we address key challenges such as lack of annotated data, large data volumes, high heterogeneity of subcellular shapes and sizes, and high inter-domain variability of cellular cryo-ET images across different experiments and contexts. Our method requires users to only select a small number of slabs from a few representative tomograms in the dataset. The core of our method is extracting features for the corresponding slabs, leveraging a Stable Diffusion foundation model pretrained on mostly natural images. The feature extraction is followed by a novel heuristic-based feature aggregation strategy, and adaptive thresholding to segment the aggregated features. The resulting masks are refined with pretrained CellPose to split composite regions, and then utilized as pseudo-ground truth for training supervised deep learning models. We validated our unsupervised foundation-model based pipeline on publicly available cryo-ET benchmark datasets, 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.
查看此版本解读 →查看此版本原文 ↗