Digital Staining of Mitochondria in Label-free Live-cell Microscopy
Ayush Somani, Arif Ahmed Sekh, Ida S. Opstad, Åsa Birna Birgisdottir, Truls Myrmel, Balpreet Singh Ahluwalia, Krishna Agarwal, Dilip K. Prasad, Alexander Horsch
The Arctic University of Norway (UiT), Tromsø
Abstract
Examining specific sub-cellular structures while minimizing cell perturbation is important in the life sciences. Fluorescence labeling and imaging is widely used for introducing specificity despite its perturbative and photo-toxic nature. With the advancement of deep learning, digital staining routines for label-free analysis have emerged as a replacement for fluorescence imaging. Nonetheless, digital staining of subcellular structures such as mitochondria is sub-optimal. This is because the models designed for computer vision are directly applied instead of optimizing them for the nature of microscopy data. We propose a new loss function with multiple thresholding steps to promote more effective learning for microscopy data. Throigh this, we demonstrate a deep learning approach to translate the label-free brightfield images of living cells into equivalent fluorescence microscopy images of mitochondria with an average structural similarity of 0.77, thus surpassing the state-of-the-art of 0.7x. Our results provide insightful examples of some unique opportunities generated by data-driven deep-learning enabled image translations.