文章基本信息

标题：MitoSegNet: Easy-to-use Deep Learning Segmentation for Analyzing Mitochondrial Morphology
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作者：Christian A. Fischer ; Laura Besora-Casals ; Stéphane G. Rolland 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2020
卷号：23
期号：10
页码：1-31
DOI：10.1016/j.isci.2020.101601
语种：English
出版社：Elsevier
摘要：SummaryWhile 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 andcatp-6ATP13A2mutantC. elegansadults. 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.Graphical AbstractDisplay OmittedHighlights•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 cellsCell Biology; Bioinformatics; Automation in Bioinformatics; Artificial Intelligence