Journal
/
Biology
/
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
JoVE Journal
Biology
Author Produced
This content is Free Access.
JoVE Journal Biology
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

DOI:
10.3791/64880-v

•

12:06 min

•

March 03, 2023

•

, , , , , , ,
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

Chapters

  • 00:11Introduction
  • 02:16Cell Culture
  • 02:47Experimental Procedure
  • 03:29Staining and Mounting of Cells on Cover Slips
  • 04:15Microscopy and Imaging
  • 04:51Generating Simulated Training Data
  • 07:02Deep Learning Based Segmentation
  • 08:58Morphological Analysis
  • 10:09Results
  • 11:42Conclusion

Summary

Automatic Translation

Automatic Translation

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

Tags

Mitochondrial MorphologySimulation Supervised LearningMicroscopy Image AnalysisMitochondria SegmentationDeep LearningCCCP TreatmentCardiomyoblasts

Article

Embed

ADD TO PLAYLIST

Usage Statistics

Related Videos

Changes in Mammary Gland Morphology and Breast Cancer Risk in Rats

Mitochondrial Isolation from Skeletal Muscle

Analyzing and Building Nucleic Acid Structures with 3DNA

Simultaneous Measurement of Mitochondrial Calcium and Mitochondrial Membrane Potential in Live Cells by Fluorescent Microscopy

Pancreatic Islet Embedding for Paraffin Sections

Improving the Accuracy of Flow Cytometric Assessment of Mitochondrial Membrane Potential in Hematopoietic Stem and Progenitor Cells Through the Inhibition of Efflux Pumps

Development of a Mobile Mitochondrial Physiology Laboratory for Measuring Mitochondrial Energetics in the Field

Integrative Toolkit to Analyze Cellular Signals: Forces, Motion, Morphology, and Fluorescence

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Read Article