ArticleComputational and structural biotechnology journal2023
A deep learning-based tool for the automated detection and analysis of caveolae in transmission electron microscopy images.
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Membrane Curvature and Cancer: Mechanisms, Implications, and Therapeutic Perspectives.Cancers · 2026Review
- Pre-trained artificial intelligence-aided analysis of nanoparticles using the segment anything model.Scientific reports · 2025Article
- Observation of biological and emulsion samples by newly developed three-dimensional impedance scanning electron microscopy.Computational and structural biotechnology journal · 2024Article
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8 authors.
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Abstract
Caveolae are nanoscopic and mechanosensitive invaginations of the plasma membrane, essential for adipocyte biology. Transmission electron microscopy (TEM) offers the highest resolution for caveolae visualization, but provides complicated images that are difficult to classify or segment using traditional automated algorithms such as threshold-based methods. As a result, the time-consuming tasks of localization and quantification of caveolae are currently performed manually. We used the Keras library in R to train a convolutional neural network with a total of 36,000 TEM image crops obtained from adipocytes previously annotated manually by an expert. The resulting model can differentiate caveolae from non-caveolae regions with a 97.44% accuracy. The predictions of this model are further processed to obtain caveolae central coordinate detection and cytoplasm boundary delimitation. The model correctly finds negligible caveolae predictions in images from caveolae depleted Cav1
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