用于复杂透射电子显微镜图像分析的深度学习驱动的自动线粒体分割。

IF 3.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Chan Jang, Hojun Lee, Jaejun Yoo, Haejin Yoon
{"title":"用于复杂透射电子显微镜图像分析的深度学习驱动的自动线粒体分割。","authors":"Chan Jang, Hojun Lee, Jaejun Yoo, Haejin Yoon","doi":"10.1038/s41598-025-03311-1","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"19076"},"PeriodicalIF":3.9000,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12125239/pdf/","citationCount":"0","resultStr":"{\"title\":\"Deep learning-driven automated mitochondrial segmentation for analysis of complex transmission electron microscopy images.\",\"authors\":\"Chan Jang, Hojun Lee, Jaejun Yoo, Haejin Yoon\",\"doi\":\"10.1038/s41598-025-03311-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":21811,\"journal\":{\"name\":\"Scientific Reports\",\"volume\":\"15 1\",\"pages\":\"19076\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-05-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12125239/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Reports\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41598-025-03311-1\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Reports","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41598-025-03311-1","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

线粒体是细胞能量产生和调节的核心,其形态与功能性能密切相关。线粒体超微结构的精确分析是理解细胞生物能量学和病理学的关键。虽然透射电子显微镜(TEM)仍然是这种分析的金标准,但传统的人工分割方法既耗时又容易出错。在这项研究中,我们引入了一种新的深度学习框架,将概率交互分割与线粒体形态的自动量化相结合。利用不确定性分析和实时用户反馈,该模型实现了相当的分割精度,同时与人工方法相比减少了90%的分析时间。在基准Lucchi++数据集和小鼠骨骼肌的真实TEM图像上进行评估,该管道不仅提高了效率,而且还确定了杜氏肌营养不良野生型和mdx小鼠模型之间线粒体形态的关键病理差异。这种自动化方法为线粒体分析提供了一种强大的、可扩展的工具,能够对细胞功能和疾病机制进行高通量和可重复的洞察。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning-driven automated mitochondrial segmentation for analysis of complex transmission electron microscopy images.

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Scientific Reports
Scientific Reports Natural Science Disciplines-
CiteScore
7.50
自引率
4.30%
发文量
19567
审稿时长
3.9 months
期刊介绍: We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections. Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021). •Engineering Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live. •Physical sciences Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics. •Earth and environmental sciences Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems. •Biological sciences Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants. •Health sciences The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信