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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Article DOI: 10.3791/64880-v • 12:06 min • March 3rd, 2023
March 3rd, 2023 •
1, 2, 1, 3, 2,4, 1, 1, 3
1Department of Computer Science, UiT The Arctic University of Norway, 2Department of Clinical Medicine, UiT The Arctic University of Norway, 3Department of Physics and Technology, UiT The Arctic University of Norway, 4Division of Cardiothoracic and Respiratory Medicine, University Hospital of North Norway

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This article explains how to use simulation-supervised machine learning for analyzing mitochondria morphology in fluorescence microscopy images of fixed cells.

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Mitochondrial Morphology Simulation Supervised Learning Machine Learning Fluorescent Microscopy Images Fixed Cells Segmentation Automatic Thresholding Otsu Manual Segmentation Signal To Background Ratio Morphological Analysis Deep Learning Ground-truth Pair Data Physics Based Simulator Microscopy Images Shape Mask Deep Learning Based Segmentation Tool Automation Parametric Curves Emitters Distribution 3D PSF (point Spread Function) Gibson-Lanni Model Photo
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