Machine learning based classification of mitochondrial morphologies from fluorescence microscopy images of Toxoplasma gondii cysts.

ABSTRACT: The mitochondrion is intimately linked to energy and overall metabolism and therefore the morphology of mitochondrion can be very informative for inferring the metabolic state of cells. In this study we report an approach for automatic classification of mitochondrial morphologies using supervised machine learning to efficiently classify them from a large number of cells at a time. Fluorescence microscopy images of the chronic encysted form of parasite Toxoplasma gondii were used for this development. Manually classifying these morphologies from the hundreds of parasites within typical tissue cysts is tedious and error prone. In addition, because of inherent biological heterogeneity in morphologies, there can be variability and lack of reproducibility in manual classification. We used image

SUBMITTER: Place BC 

PROVIDER: S-EPMC9894464 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature