Digital Staining of Mitochondria in Label-free Live-cell Microscopy

Examining specific sub-cellular structures while minimizing cell perturbation is important in the life sciences. Fluorescence labeling and imaging is widely used. With the advancement of deep learning, digital staining routines for label-free analysis have emerged to replace fluorescence imaging. Nonetheless, digital staining of sub-cellular structures such as mitochondria is sub-optimal. This is because the models designed for computer vision are directly applied instead of optimizing them for microscopy data. We propose a new loss function with multiple thresholding steps to promote more effective learning for microscopy data. We demonstrate a deep learning approach to translate the labelfree brightfield images of living cells into equivalent fluorescence images of mitochondria with an average structural similarity of 0.77, thus surpassing the state-of-the-art of 0.7 with L1. We provide insightful examples of unique opportunities by data-driven deep learning-enabled image translations.