ArticleFrontiers in neuroscience2020
Automatic Reconstruction of Mitochondria and Endoplasmic Reticulum in Electron Microscopy Volumes by Deep Learning.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 52 citations in OpenAlex.
- Cell Structure Segmentation in TEM Images of Murine Skin Melanoma Cells by Deep Learning Model.Journal of imaging · 2026Article
- DeepSCEM: A User-Friendly Solution for Deep Learning-Based Image Segmentation in Cellular Electron Microscopy.Biology of the cell · 2025Article
- Review
- Transfer learning improves performance in volumetric electron microscopy organelle segmentation across tissues.Bioinformatics advances · 2025Article
- Machine learning-based 3D segmentation of mitochondria in polarized epithelial cells.Mitochondrion · 2024Article
- Dysregulation of cellular membrane homeostasis as a crucial modulator of cancer risk.The FEBS journal · 2024Review
- A semi-automatic method for extracting mitochondrial cristae characteristics from 3D focused ion beam scanning electron microscopy data.Communications biology · 2024Article
- Impact of photobleaching on quantitative, spatio-temporal, super-resolution imaging of mitochondria in liveNpj imaging · 2024Article
- ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology.Nature methods · 2023Article
- Deep neural network automated segmentation of cellular structures in volume electron microscopy.The Journal of cell biology · 2023Article
- Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset.Cell systems · 2023Article
- A 3D analysis revealed complexe mitochondria morphologies in porcine cumulus cells.Scientific reports · 2022Article
- DeepContact: High-throughput quantification of membrane contact sites based on electron microscopy imaging.The Journal of cell biology · 2022Article
- How innovations in methodology offer new prospects for volume electron microscopy.Journal of microscopy · 2022Review
- Quantifying Regulated Mitochondrial Fission in Macrophages.Methods in molecular biology (Clifton, N.J.) · 2022Article
- VGG-UNet/VGG-SegNet Supported Automatic Segmentation of Endoplasmic Reticulum Network in Fluorescence Microscopy Images.Scanning · 2022Article
- Three-dimensional ATUM-SEM reconstruction and analysis of hepatic endoplasmic reticulum‒organelle interactions.Journal of molecular cell biology · 2021Article
- Article
- Article
- Interorganelle communication, aging, and neurodegeneration.Genes & development · 2021Review
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Together, mitochondria and the endoplasmic reticulum (ER) occupy more than 20% of a cell's volume, and morphological abnormality may lead to cellular function disorders. With the rapid development of large-scale electron microscopy (EM), manual contouring and three-dimensional (3D) reconstruction of these organelles has previously been accomplished in biological studies. However, manual segmentation of mitochondria and ER from EM images is time consuming and thus unable to meet the demands of large data analysis. Here, we propose an automated pipeline for mitochondrial and ER reconstruction, including the mitochondrial and ER contact sites (MAMs). We propose a novel recurrent neural network to detect and segment mitochondria and a fully residual convolutional network to reconstruct the ER. Based on the sparse distribution of synapses, we use mitochondrial context information to rectify the local misleading results and obtain 3D mitochondrial reconstructions. The experimental results demonstrate that the proposed method achieves state-of-the-art performance.
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