Papers › CEM500K – A large-scale heterogeneous unlabeled cellular electron microscopy image...

CEM500K – A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning

11 Dec 2020archive 2025-07-28

Ryan W Conrad, Kedar Narayan

Automated segmentation of cellular electron microscopy (EM) datasets remains a challenge. Supervised deep learning (DL) methods that rely on region-of-interest (ROI) annotations yield models that fail to generalize to unrelated datasets. Newer unsupervised DL algorithms require relevant pre-training images, however, pre-training on currently available EM datasets is computationally expensive and shows little value for unseen biological contexts, as these datasets are large and homogeneous. To address this issue, we present CEM500K, a nimble 25 GB dataset of 500,000 unique cellular EM images curated from nearly 600 three-dimensional (3D) and 10,000 two-dimensional (2D) images from >100 unrelated imaging projects. We show that models pre-trained on CEM500K learn features that are biologically relevant and resilient to meaningful image augmentations. Critically, we evaluate transfer learning from these pre-trained models on six publicly available and one newly derived benchmark segmentation task and report state-of-the-art results on each. We release the CEM500K dataset, pre-trained models and curation pipeline for model building and further expansion by the EM community.

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Tasks

Electron Microscopy Image Segmentation Segmentation Transfer Learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Electron Microscopy Image Segmentation	EMOrganelles	CEM500K-moco	Average IOU	0.7	#1 of 1	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block Concatenated Skip Connection Convolution Dense Connections Feedforward Network Global Average Pooling InfoNCE Kaiming Initialization Max Pooling MoCo v2 Random Gaussian Blur ReLU Residual Block Residual Connection U-Net

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