ArticleeLife2021
CEM500K, a large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Cell Structure Segmentation in TEM Images of Murine Skin Melanoma Cells by Deep Learning Model.Journal of imaging · 2026Article
- NucleoNet and DropNet: Generalist deep learning models for instance segmentation of nuclei and lipid droplets from electron microscopy images.bioRxiv : the preprint server for biology · 2026Article
- MIFA: Metadata, Incentives, Formats and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis.Nature methods · 2025Review
- DeepEM Playground: Bringing deep learning to electron microscopy labs.Journal of microscopy · 2025Article
- Deep learning-driven automated mitochondrial segmentation for analysis of complex transmission electron microscopy images.Scientific reports · 2025Article
- RETINA: Reconstruction-based pre-trained enhanced TransUNet for electron microscopy segmentation on the CEM500K dataset.PLoS computational biology · 2025Article
- Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets.Nature protocols · 2024Review
- Morphomics via next-generation electron microscopy.Journal of molecular cell biology · 2024Review
- Electron Microscopy Techniques for 3D Plant ER Imaging.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Current Progress and Challenges in Large-Scale 3D Mitochondria Instance Segmentation.IEEE transactions on medical imaging · 2023Article
- Genetically encoded barcodes for correlative volume electron microscopy.Nature biotechnology · 2023Article
- 3D surface reconstruction of cellular cryo-soft X-ray microscopy tomograms using semisupervised deep learning.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset.Cell systems · 2023Article
- How innovations in methodology offer new prospects for volume electron microscopy.Journal of microscopy · 2022Review
- Volume electron microscopy.Nature reviews. Methods primers · 2022Article
- U-RISC: An Annotated Ultra-High-Resolution Electron Microscopy Dataset Challenging the Existing Deep Learning Algorithms.Frontiers in computational neuroscience · 2022Article
- Volumetric Semantic Instance Segmentation of the Plasma Membrane of HeLa Cells.Journal of imaging · 2021Article
- Review
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2 authors.
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Abstract
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 0.5 × 10
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