Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Abstract

The quantitative analysis of subcellular organelles such as mitochondria in cell fluorescence microscopy images is a demanding task because of the inherent challenges in the segmentation of these small and morphologically diverse structures. In this article, we demonstrate the use of a machine learning-aided segmentation and analysis pipeline for the quantification of mitochondrial morphology in fluorescence microscopy images of fixed cells. The deep learning-based segmentation tool is trained on simulated images and eliminates the requirement for ground truth annotations for supervised deep learning. We demonstrate the utility of this tool on fluorescence microscopy images of fixed cardiomyoblasts with a stable expression of fluorescent mitochondria markers and employ specific cell culture conditions to induce changes in the mitochondrial morphology.