ArticleBioinformatics (Oxford, England)2021
EM-stellar: benchmarking deep learning for electron microscopy image segmentation.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Deep Learning-Driven Transmission Electron Microscopy Analysis of Murine Optic Nerve Myelinated Axons.Ophthalmology science · 2026Article
- A transmission electron microscopy platform for assessing mitochondrial and nuclear architecture in cardiomyocytes.Cell reports methods · 2025Article
- DeepEM Playground: Bringing deep learning to electron microscopy labs.Journal of microscopy · 2025Article
- RETINA: Reconstruction-based pre-trained enhanced TransUNet for electron microscopy segmentation on the CEM500K dataset.PLoS computational biology · 2025Article
- Article
- PS-Net: human perception-guided segmentation network for EM cell membrane.Bioinformatics (Oxford, England) · 2023Article
- Unravelling cell migration: defining movement from the cell surface.Cell adhesion & migration · 2022Review
- CardioVinci: building blocks for virtual cardiac cells using deep learning.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Article
- Weighted average ensemble-based semantic segmentation in biological electron microscopy images.Histochemistry and cell biology · 2022Article
- Democratising deep learning for microscopy with ZeroCostDL4Mic.Nature communications · 2021Article
- EM-stellar: benchmarking deep learning for electron microscopy image segmentation.Bioinformatics (Oxford, England) · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
motivationThe inherent low contrast of electron microscopy (EM) datasets presents a significant challenge for rapid segmentation of cellular ultrastructures from EM data. This challenge is particularly prominent when working with high-resolution big-datasets that are now acquired using electron tomography and serial block-face imaging techniques. Deep learning (DL) methods offer an exciting opportunity to automate the segmentation process by learning from manual annotations of a small sample of EM data. While many DL methods are being rapidly adopted to segment EM data no benchmark analysis has been conducted on these methods to date.
resultsWe present EM-stellar, a platform that is hosted on Google Colab that can be used to benchmark the performance of a range of state-of-the-art DL methods on user-provided datasets. Using EM-stellar we show that the performance of any DL method is dependent on the properties of the images being segmented. It also follows that no single DL method performs consistently across all performance evaluation metrics. AVAILABILITY AND IMPLEMENTATION: EM-stellar (code and data) is written in Python and is freely available under MIT license on GitHub (https://github.com/cellsmb/em-stellar). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.