ArticlePlant physiology2021
DeepLearnMOR: a deep-learning framework for fluorescence image-based classification of organelle morphology.
Article in Plant physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- HiExM Enables Scalable Mapping of Organelle Morphology and Spatial Heterogeneity.bioRxiv : the preprint server for biology · 2026Article
- Data-independent fluorescence molecular imaging analysis: a two-stage clustering framework for intraoperative tumor imaging.Biomedical engineering online · 2026Article
- Mitochondrial fission and fusion in inflammatory diseases: mechanisms and therapeutic implications.Journal of translational medicine · 2025Review
- DeepD&Cchl: an AI tool for automated 3D single-cell chloroplast detection, counting, and cell type clustering.Frontiers in plant science · 2025Article
- A century journey of organelles research in the plant endomembrane system.The Plant cell · 2024Review
- From Organelle Morphology to Whole-Plant Phenotyping: A Phenotypic Detection Method Based on Deep Learning.Plants (Basel, Switzerland) · 2024Article
- Plant microphenotype: from innovative imaging to computational analysis.Plant biotechnology journal · 2024Review
- On-chip label-free cell classification based directly on off-axis holograms and spatial-frequency-invariant deep learning.Scientific reports · 2023Article
- Editorial: Post-translational mechanisms involved in regulating peroxisome biogenesis, functions and organelle-crosstalk.Frontiers in cell and developmental biology · 2023Article
- Advanced Image Analysis Methods for Automated Segmentation of Subnuclear Chromatin Domains.Epigenomes · 2022Article
- Image-Based Analysis Revealing the Molecular Mechanism of Peroxisome Dynamics in Plants.Frontiers in cell and developmental biology · 2022Review
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Authors and funding
6 authors.
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Abstract
The proper biogenesis, morphogenesis, and dynamics of subcellular organelles are essential to their metabolic functions. Conventional techniques for identifying, classifying, and quantifying abnormalities in organelle morphology are largely manual and time-consuming, and require specific expertise. Deep learning has the potential to revolutionize image-based screens by greatly improving their scope, speed, and efficiency. Here, we used transfer learning and a convolutional neural network (CNN) to analyze over 47,000 confocal microscopy images from Arabidopsis wild-type and mutant plants with abnormal division of one of three essential energy organelles: chloroplasts, mitochondria, or peroxisomes. We have built a deep-learning framework, DeepLearnMOR (Deep Learning of the Morphology of Organelles), which can rapidly classify image categories and identify abnormalities in organelle morphology with over 97% accuracy. Feature visualization analysis identified important features used by the CNN to predict morphological abnormalities, and visual clues helped to better understand the decision-making process, thereby validating the reliability and interpretability of the neural network. This framework establishes a foundation for future larger-scale research with broader scopes and greater data set diversity and heterogeneity.
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