Hannah Kniesel, Poonam Poonam, Tristan Payer, Tim Bergner, Pedro Hermosilla, Timo Ropinski
{"title":"DeepEM Playground:将深度学习带入电子显微镜实验室。","authors":"Hannah Kniesel, Poonam Poonam, Tristan Payer, Tim Bergner, Pedro Hermosilla, Timo Ropinski","doi":"10.1111/jmi.70005","DOIUrl":null,"url":null,"abstract":"<p>Deep learning (DL) has transformed image analysis, enabling breakthroughs in segmentation, object detection, and classification. However, a gap persists between cutting-edge DL research and its practical adoption in electron microscopy (EM) labs. This is largely due to the inaccessibility of DL methods for EM specialists and the expertise required to interpret model outputs.</p><p>To bridge this gap, we introduce DeepEM Playground, an interactive, user-friendly platform designed to empower EM researchers – regardless of coding experience – to train, tune, and apply DL models. By providing a guided, hands-on approach, DeepEM Playground enables users to explore the workings of DL in EM, facilitating both first-time engagement and more advanced model customisation.</p><p>The DeepEM Playground lowers the barrier to entry and fosters a deeper understanding of deep learning, thereby enabling the EM community to integrate AI-driven analysis into their workflows more confidently and effectively.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":"299 3","pages":"287-300"},"PeriodicalIF":1.9000,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jmi.70005","citationCount":"0","resultStr":"{\"title\":\"DeepEM Playground: Bringing deep learning to electron microscopy labs\",\"authors\":\"Hannah Kniesel, Poonam Poonam, Tristan Payer, Tim Bergner, Pedro Hermosilla, Timo Ropinski\",\"doi\":\"10.1111/jmi.70005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Deep learning (DL) has transformed image analysis, enabling breakthroughs in segmentation, object detection, and classification. However, a gap persists between cutting-edge DL research and its practical adoption in electron microscopy (EM) labs. This is largely due to the inaccessibility of DL methods for EM specialists and the expertise required to interpret model outputs.</p><p>To bridge this gap, we introduce DeepEM Playground, an interactive, user-friendly platform designed to empower EM researchers – regardless of coding experience – to train, tune, and apply DL models. By providing a guided, hands-on approach, DeepEM Playground enables users to explore the workings of DL in EM, facilitating both first-time engagement and more advanced model customisation.</p><p>The DeepEM Playground lowers the barrier to entry and fosters a deeper understanding of deep learning, thereby enabling the EM community to integrate AI-driven analysis into their workflows more confidently and effectively.</p>\",\"PeriodicalId\":16484,\"journal\":{\"name\":\"Journal of microscopy\",\"volume\":\"299 3\",\"pages\":\"287-300\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2025-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jmi.70005\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of microscopy\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jmi.70005\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MICROSCOPY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of microscopy","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jmi.70005","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MICROSCOPY","Score":null,"Total":0}
DeepEM Playground: Bringing deep learning to electron microscopy labs
Deep learning (DL) has transformed image analysis, enabling breakthroughs in segmentation, object detection, and classification. However, a gap persists between cutting-edge DL research and its practical adoption in electron microscopy (EM) labs. This is largely due to the inaccessibility of DL methods for EM specialists and the expertise required to interpret model outputs.
To bridge this gap, we introduce DeepEM Playground, an interactive, user-friendly platform designed to empower EM researchers – regardless of coding experience – to train, tune, and apply DL models. By providing a guided, hands-on approach, DeepEM Playground enables users to explore the workings of DL in EM, facilitating both first-time engagement and more advanced model customisation.
The DeepEM Playground lowers the barrier to entry and fosters a deeper understanding of deep learning, thereby enabling the EM community to integrate AI-driven analysis into their workflows more confidently and effectively.
期刊介绍:
The Journal of Microscopy is the oldest journal dedicated to the science of microscopy and the only peer-reviewed publication of the Royal Microscopical Society. It publishes papers that report on the very latest developments in microscopy such as advances in microscopy techniques or novel areas of application. The Journal does not seek to publish routine applications of microscopy or specimen preparation even though the submission may otherwise have a high scientific merit.
The scope covers research in the physical and biological sciences and covers imaging methods using light, electrons, X-rays and other radiations as well as atomic force and near field techniques. Interdisciplinary research is welcome. Papers pertaining to microscopy are also welcomed on optical theory, spectroscopy, novel specimen preparation and manipulation methods and image recording, processing and analysis including dynamic analysis of living specimens.
Publication types include full papers, hot topic fast tracked communications and review articles. Authors considering submitting a review article should contact the editorial office first.