MitoSegNet: Easy-to-use Deep Learning Segmentation for Analyzing Mitochondrial Morphology

While the analysis of mitochondrial morphology has emerged as a key tool in the study of mitochondrial function, efficient quantification of mitochondrial microscopy images presents a challenging task and bottleneck for statistically robust conclusions. Here, we present Mitochondrial Segmentation Ne...

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Bibliographic Details
Published in:iScience 2020-10, Vol.23 (10), p.101601, Article 101601
Main Authors: Fischer, Christian A., Besora-Casals, Laura, Rolland, Stéphane G., Haeussler, Simon, Singh, Kritarth, Duchen, Michael, Conradt, Barbara, Marr, Carsten
Format: Article
Language:English
Subjects:
Artificial Intelligence
Automation in Bioinformatics
Bioinformatics
Cell Biology
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Summary:While the analysis of mitochondrial morphology has emerged as a key tool in the study of mitochondrial function, efficient quantification of mitochondrial microscopy images presents a challenging task and bottleneck for statistically robust conclusions. Here, we present Mitochondrial Segmentation Network (MitoSegNet), a pretrained deep learning segmentation model that enables researchers to easily exploit the power of deep learning for the quantification of mitochondrial morphology. We tested the performance of MitoSegNet against three feature-based segmentation algorithms and the machine-learning segmentation tool Ilastik. MitoSegNet outperformed all other methods in both pixelwise and morphological segmentation accuracy. We successfully applied MitoSegNet to unseen fluorescence microscopy images of mitoGFP expressing mitochondria in wild-type and catp-6ATP13A2 mutant C. elegans adults. Additionally, MitoSegNet was capable of accurately segmenting mitochondria in HeLa cells treated with fragmentation inducing reagents. We provide MitoSegNet in a toolbox for Windows and Linux operating systems that combines segmentation with morphological analysis. [Display omitted] •MitoSegNet is a deep learning model for quantifying mitochondrial morphology•MitoSegNet outperforms other mitochondria segmentation methods•MitoSegNet was used to identify previously unreported morphological differences•MitoSegNet successfully segments mitochondria in HeLa cells Cell Biology; Bioinformatics; Automation in Bioinformatics; Artificial Intelligence
ISSN:2589-0042
2589-0042
DOI:10.1016/j.isci.2020.101601