Deep learning-driven automated mitochondrial segmentation for analysis of complex transmission electron microscopy images
Abstract Mitochondria are central to cellular energy production and regulation, with their morphology tightly linked to functional performance. Precise analysis of mitochondrial ultrastructure is crucial for understanding cellular bioenergetics and pathology. While transmission electron microscopy (...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-03311-1 |
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| author | Chan Jang Hojun Lee Jaejun Yoo Haejin Yoon |
| author_facet | Chan Jang Hojun Lee Jaejun Yoo Haejin Yoon |
| author_sort | Chan Jang |
| collection | DOAJ |
| description | Abstract Mitochondria are central to cellular energy production and regulation, with their morphology tightly linked to functional performance. Precise analysis of mitochondrial ultrastructure is crucial for understanding cellular bioenergetics and pathology. While transmission electron microscopy (TEM) remains the gold standard for such analyses, traditional manual segmentation methods are time-consuming and prone to error. In this study, we introduce a novel deep learning framework that combines probabilistic interactive segmentation with automated quantification of mitochondrial morphology. Leveraging uncertainty analysis and real-time user feedback, the model achieves comparable segmentation accuracy while reducing analysis time by 90% compared to manual methods. Evaluated on both benchmark Lucchi++ datasets and real-world TEM images of mouse skeletal muscle, the pipeline not only improved efficiency but also identified key pathological differences in mitochondrial morphology between wild-type and mdx mouse models of Duchenne muscular dystrophy. This automated approach offers a powerful, scalable tool for mitochondrial analysis, enabling high-throughput and reproducible insights into cellular function and disease mechanisms. |
| format | Article |
| id | doaj-art-5bcbb4a9488a4e5585ed236543da5e8a |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5bcbb4a9488a4e5585ed236543da5e8a2025-08-20T02:00:00ZengNature PortfolioScientific Reports2045-23222025-05-0115111310.1038/s41598-025-03311-1Deep learning-driven automated mitochondrial segmentation for analysis of complex transmission electron microscopy imagesChan Jang0Hojun Lee1Jaejun Yoo2Haejin Yoon3Graduate School of Artificial Intelligence, Ulsan National Institute of Science & TechnologyDepartment of Biological Sciences, Ulsan National Institute of Science & TechnologyGraduate School of Artificial Intelligence, Ulsan National Institute of Science & TechnologyDepartment of Biological Sciences, Ulsan National Institute of Science & TechnologyAbstract Mitochondria are central to cellular energy production and regulation, with their morphology tightly linked to functional performance. Precise analysis of mitochondrial ultrastructure is crucial for understanding cellular bioenergetics and pathology. While transmission electron microscopy (TEM) remains the gold standard for such analyses, traditional manual segmentation methods are time-consuming and prone to error. In this study, we introduce a novel deep learning framework that combines probabilistic interactive segmentation with automated quantification of mitochondrial morphology. Leveraging uncertainty analysis and real-time user feedback, the model achieves comparable segmentation accuracy while reducing analysis time by 90% compared to manual methods. Evaluated on both benchmark Lucchi++ datasets and real-world TEM images of mouse skeletal muscle, the pipeline not only improved efficiency but also identified key pathological differences in mitochondrial morphology between wild-type and mdx mouse models of Duchenne muscular dystrophy. This automated approach offers a powerful, scalable tool for mitochondrial analysis, enabling high-throughput and reproducible insights into cellular function and disease mechanisms.https://doi.org/10.1038/s41598-025-03311-1Mitochondrial morphologyDeep learning segmentationTransmission electron microscopy imagingInteractive segmentationUncertainty analysisAutomated quantification |
| spellingShingle | Chan Jang Hojun Lee Jaejun Yoo Haejin Yoon Deep learning-driven automated mitochondrial segmentation for analysis of complex transmission electron microscopy images Scientific Reports Mitochondrial morphology Deep learning segmentation Transmission electron microscopy imaging Interactive segmentation Uncertainty analysis Automated quantification |
| title | Deep learning-driven automated mitochondrial segmentation for analysis of complex transmission electron microscopy images |
| title_full | Deep learning-driven automated mitochondrial segmentation for analysis of complex transmission electron microscopy images |
| title_fullStr | Deep learning-driven automated mitochondrial segmentation for analysis of complex transmission electron microscopy images |
| title_full_unstemmed | Deep learning-driven automated mitochondrial segmentation for analysis of complex transmission electron microscopy images |
| title_short | Deep learning-driven automated mitochondrial segmentation for analysis of complex transmission electron microscopy images |
| title_sort | deep learning driven automated mitochondrial segmentation for analysis of complex transmission electron microscopy images |
| topic | Mitochondrial morphology Deep learning segmentation Transmission electron microscopy imaging Interactive segmentation Uncertainty analysis Automated quantification |
| url | https://doi.org/10.1038/s41598-025-03311-1 |
| work_keys_str_mv | AT chanjang deeplearningdrivenautomatedmitochondrialsegmentationforanalysisofcomplextransmissionelectronmicroscopyimages AT hojunlee deeplearningdrivenautomatedmitochondrialsegmentationforanalysisofcomplextransmissionelectronmicroscopyimages AT jaejunyoo deeplearningdrivenautomatedmitochondrialsegmentationforanalysisofcomplextransmissionelectronmicroscopyimages AT haejinyoon deeplearningdrivenautomatedmitochondrialsegmentationforanalysisofcomplextransmissionelectronmicroscopyimages |