Virtual labeling of mitochondria in living cells using correlative imaging and physics-guided deep learning

Author:

Somani AyushORCID,Ahmed Sekh Arif1,Opstad Ida S.2ORCID,Birna Birgisdottir Åsa2,Myrmel Truls2,Singh Ahluwalia Balpreet2ORCID,Horsch Alexander,Agarwal Krishna2,Prasad Dilip K.ORCID

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

Publisher

Optica Publishing Group

Subject

Atomic and Molecular Physics, and Optics,Biotechnology

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