Artificial intelligence approaches to the volumetric quantification of glycogen granules in EM images of human tissue.

ABSTRACT: Skeletal muscle, the major processor of dietary glucose, stores it in myriad glycogen granules. Their numbers vary with cellular location and physiological and pathophysiological states. AI models were developed to derive granular glycogen content from electron-microscopic images of human muscle. Two UNet-type semantic segmentation models were built: "Locations" classified pixels as belonging to different regions in the cell; "Granules" identified pixels within granules. From their joint output, a pixel fraction pf was calculated for images from patients positive (MHS) or negative (MHN) to a test for malignant hyperthermia susceptibility. pf was used to derive vf, the volume fraction occupied by granules. The relationship vf (pf) was derived from a simulation of volumes ("baskets") contain

SUBMITTER: Rios E 

PROVIDER: S-EPMC11233403 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature