Abstract
Understanding the physiological processes underlying cardiovascular disease (CVD) requires examination of endothelial cell (EC) mitochondrial networks, because mitochondrial function and adenosine triphosphate production are crucial in EC metabolism, and consequently influence CVD progression. Although current biochemical assays and immunofluorescence microscopy can reveal how mitochondrial function influences cellular metabolism, they cannot achieve live observation and tracking changes in mitochondrial networks through fusion and fission events. Holotomographic microscopy (HTM) has emerged as a promising technique for real-time, label-free visualization of ECs and their organelles, such as mitochondria. This nondestructive, noninterfering live cell imaging method offers unprecedented opportunities to observe mitochondrial network dynamics. However, because existing image processing tools based on immunofluorescence microscopy techniques are incompatible with HTM images, a machine-learning model is required. Here, we developed a model using a U-net learner with a Resnet18 encoder to identify four classes within HTM images: mitochondrial networks, cell borders, ECs, and background. This method accurately identifies mitochondrial structures and positions. With high accuracy and similarity metrics, the output image successfully provides visualization of mitochondrial networks within HTM images of ECs. This approach enables the study of mitochondrial networks and their effects, and holds promise in advancing understanding of CVD mechanisms.