ArticleiScience2020
MitoSegNet: Easy-to-use Deep Learning Segmentation for Analyzing Mitochondrial Morphology.
Article in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Advanced Open-Source Experimental-Design Tools for Microplate-Based Assays with Acoustic Liquid Handling.bioRxiv : the preprint server for biology · 2026Article
- Fmp30p is a mitochondrial phosphatidylinositol hydrolase that modulates CoQ biosynthesis.Nature communications · 2026Article
- RoundMi: A quantitative method to analyze mitochondrial morphology in mitotic cells.FEBS open bio · 2026Article
- WeakMitoSAM: competitive prompt aggregation for point-supervised mitochondria segmentation in electron microscopy images.Biomedical optics express · 2026Article
- Adulthood depletion of Integrator extends lifespan and healthspan via defective pre-mRNA processing.bioRxiv : the preprint server for biology · 2026Article
- 3D Mitochondria Shape Library for Optical Microscopy (3DMSL): A multimodal dataset for deep learning based mitochondrial analysis.Data in brief · 2026Article
- CabioRxiv : the preprint server for biology · 2026Article
- Identification and Ultrastructural Peculiarities of Nestin-Carrying Progenitor Cells in Kidney.International journal of molecular sciences · 2026Article
- Systematic evaluation of mitochondrial morphology regulators for amelioration of neuronal α-synucleinopathy.NPJ Parkinson's disease · 2026Article
- MitoTex (Mitochondria Texture Analysis User Interface): Open-Source Framework for Textural Characterization and Classification of Mitochondrial Structures.International journal of molecular sciences · 2026Article
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- Deep learning analysis of urine-derived stem cell mitochondrial morphology as a non-invasive Alzheimer's disease biomarker.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2026Article
- Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy.PLoS computational biology · 2025Article
- Single Mitochondrion Morphology-Function Relationship Analysis Using Fluorescent Probes and Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Dominant negative ATP5F1A variants disrupt oxidative phosphorylation causing neurological disorders.EMBO molecular medicine · 2025Article
- Image-based identification and isolation of micronucleated cells to dissect cellular consequences.eLife · 2025Article
- Deep learning-driven automated mitochondrial segmentation for analysis of complex transmission electron microscopy images.Scientific reports · 2025Article
- VPS13D mutations affect mitochondrial homeostasis and locomotion in Caenorhabditis elegans.G3 (Bethesda, Md.) · 2025Article
- Nellie: automated organelle segmentation, tracking and hierarchical feature extraction in 2D/3D live-cell microscopy.Nature methods · 2025Article
- Mitochondrial segmentation and function prediction in live-cell images with deep learning.Nature communications · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
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
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.