ArticleScientific reports2019
Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Application of extracellular vesicles in the CRISPR-based diagnosis and treatment: possibilities and challenges.Journal of biological engineering · 2026Review
- Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.Communications engineering · 2026Review
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- Review
- SynapseNet: Deep learning for automatic synapse reconstruction.Molecular biology of the cell · 2025Article
- Quantum machine learning-based electrokinetic mining for the identification of nanoparticles and exosomes with minimal training data.Bioactive materials · 2025Article
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
- Small Extracellular Vesicles Promote Stiffness-mediated Metastasis.Cancer research communications · 2024Article
- Translational Challenges and Prospective Solutions in the Implementation of Biomimetic Delivery Systems.Pharmaceutics · 2023Review
- Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks.Bioinformatics (Oxford, England) · 2023Article
- Plantorganelle Hunter is an effective deep-learning-based method for plant organelle phenotyping in electron microscopy.Nature plants · 2023Article
- [A region-level contrastive learning-based deep model for glomerular ultrastructure segmentation on electron microscope images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
- Quantitative imaging of vesicle-protein interactions reveals close cooperation among proteins.Journal of extracellular vesicles · 2023Article
- Article
- How innovations in methodology offer new prospects for volume electron microscopy.Journal of microscopy · 2022Review
- AxonDeep: Automated Optic Nerve Axon Segmentation in Mice With Deep Learning.Translational vision science & technology · 2021Article
- Evaluating Very Deep Convolutional Neural Networks for Nucleus Segmentation from Brightfield Cell Microscopy Images.SLAS discovery : advancing life sciences R & D · 2021Article
- DeepImageJ: A user-friendly environment to run deep learning models in ImageJ.Nature methods · 2021Article
- Deep-learning in situ classification of HIV-1 virion morphology.Computational and structural biotechnology journal · 2021Article
- Brain Ultrastructure: Putting the Pieces Together.Frontiers in cell and developmental biology · 2021Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Small extracellular vesicles (sEVs) are cell-derived vesicles of nanoscale size (~30-200 nm) that function as conveyors of information between cells, reflecting the cell of their origin and its physiological condition in their content. Valuable information on the shape and even on the composition of individual sEVs can be recorded using transmission electron microscopy (TEM). Unfortunately, sample preparation for TEM image acquisition is a complex procedure, which often leads to noisy images and renders automatic quantification of sEVs an extremely difficult task. We present a completely deep-learning-based pipeline for the segmentation of sEVs in TEM images. Our method applies a residual convolutional neural network to obtain fine masks and use the Radon transform for splitting clustered sEVs. Using three manually annotated datasets that cover a natural variability typical for sEV studies, we show that the proposed method outperforms two different state-of-the-art approaches in terms of detection and segmentation performance. Furthermore, the diameter and roundness of the segmented vesicles are estimated with an error of less than 10%, which supports the high potential of our method in biological applications.
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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.