A deep learning-based tool for the automated detection and analysis of caveolae in transmission electron microscopy images.

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 proc

SUBMITTER: Aboy-Pardal MCM 

PROVIDER: S-EPMC9755247 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature