3.Mitochondria mask generation: The binary image was further processed by subtracting 254 from all pixel values, resulting in a binary mitochondria mask. This manipulation ensured that mitochondrial pixels were represented as 1, while the background was set to 0. 4.Background subtraction: The generated mask image was multiplied with the original 16-bit image, effectively removing the background and facilitating accurate quantification of mitochondria. 5.Auto-thresholding: Auto-thresholding algorithms were then applied to the background corrected image. Allowing for the automated determination of intensity thresholds to distinguish between mitochondrial and non-mitochondrial regions. 6.Region-of-interest (ROI) selection: To quantify the mitochondria, a manual ROI selection was performed. Using the Polygon Selection tool, the cell boundary was traced by creating a series of connected points. This ensured that the ROI enclosed the entire cell of interest.