analyzing-mitochondrial-morphology-through-simulation-supervised

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

The quantitative analysis of subcellular organelles such as mitochondria in cell fluorescence microscopy images is a demanding task because of the ...

UiT The Arctic University of Norway

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.

Ayush Somani

Department of Computer Science

UiT The Arctic University of Norway

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Ayush Somani

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Computer Science

UiT The Arctic University of Norway

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Department of Computer Science