Affiliation:
1. XIM University
2. UiT The Arctic University of Norway
Abstract
Mitochondria play a crucial role in cellular metabolism. This paper presents a novel method to visualize mitochondria in living cells without the use of fluorescent markers. We propose a physics-guided deep learning approach for obtaining virtually labeled micrographs of mitochondria from bright-field images. We integrate a microscope’s point spread function in the learning of an adversarial neural network for improving virtual labeling. We show results (average Pearson correlation 0.86) significantly better than what was achieved by state-of-the-art (0.71) for virtual labeling of mitochondria. We also provide new insights into the virtual labeling problem and suggest additional metrics for quality assessment. The results show that our virtual labeling approach is a powerful way of segmenting and tracking individual mitochondria in bright-field images, results previously achievable only for fluorescently labeled mitochondria.
Funder
Universitetet i Tromsø
Norges Forskningsråd
H2020 Future and Emerging Technologies
H2020 Excellent Science
Subject
Atomic and Molecular Physics, and Optics,Biotechnology
Cited by
4 articles.
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