Nature Methods<\/em>.<\/p>\n\n\n\n\u201cUltimately, we were able to take \u2018the best of both worlds\u2019 in this approach,\u201d says Nils Wagner, one of the paper\u2019s two lead authors and now a PhD student at the Technical University of Munich. \u201cAI enabled us to combine different microscopy techniques, so that we could image as fast as light-field microscopy allows and get close to the image resolution of light-sheet microscopy.\u201d<\/p>\n\n\n\n
Although light-sheet microscopy and light-field microscopy sound similar, these techniques have different advantages and challenges. Light-field microscopy captures large 3D images that allow researchers to track and measure remarkably fine movements, such as a fish larva\u2019s beating heart, at very high speeds. But this technique produces massive amounts of data, which can take days to process, and the final images usually lack resolution.<\/p>\n\n\n\n
Light-sheet microscopy homes in on a single 2D plane of a given sample at one time, so researchers can image samples at higher resolution. Compared with light-field microscopy, light-sheet microscopy produces images that are quicker to process, but the data are not as comprehensive, since they only capture information from a single 2D plane at a time.<\/p>\n\n\n\n
To take advantage of the benefits of each technique, EMBL researchers developed an approach that uses light-field microscopy to image large 3D samples and light-sheet microscopy to train the AI algorithms, which then create an accurate 3D picture of the sample.<\/p>\n\n\n\n
\u201cIf you build algorithms that produce an image, you need to check that these algorithms are constructing the right image,\u201d explains Anna Kreshuk, the EMBL group leader whose team brought machine learning expertise to the project. In the new study, the researchers used light-sheet microscopy to make sure the AI algorithms were working, Anna says. \u201cThis makes our research stand out from what has been done in the past.\u201d<\/p>\n\n\n\n
Robert Prevedel, the EMBL group leader whose group contributed the novel hybrid microscopy platform, notes that the real bottleneck in building better microscopes often isn\u2019t optics technology, but computation. That\u2019s why, back in 2018, he and Anna decided to join forces. \u201cOur method will be really key for people who want to study how brains compute. Our method can image an entire brain of a fish larva, in real time,\u201d Robert says.<\/p>\n\n\n\n
He and Anna say this approach could potentially be modified to work with different types of microscopes too, eventually allowing biologists to look at dozens of different specimens and see much more, much faster. For example, it could help to find genes that are involved in heart development, or could measure the activity of thousands of neurons at the same time.<\/p>\n\n\n\n
Next, the researchers plan to explore whether the method can be applied to larger species, including mammals.<\/p>\n\n\n\n
Study co-lead author Fynn Beuttenm\u00fcller, a PhD student in the Kreshuk group at EMBL Heidelberg, has no doubts about the power of AI. \u201cComputational methods will continue to bring exciting advances to microscopy.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"
EMBL scientists have combined artificial intelligence (AI) algorithms with two cutting-edge microscopy techniques.<\/p>\n","protected":false},"author":100,"featured_media":37924,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[2,17591],"tags":[4718,602,9960,667,604,1313,859,464,931],"embl_taxonomy":[9796,19313,19357],"class_list":["post-37920","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-science","category-science-technology","tag-artificial-intelligence","tag-kreshuk","tag-light-field-microscopy","tag-light-sheet-microscopy","tag-machine-learning","tag-medaka","tag-microscope","tag-prevedel","tag-zebrafish","embl_taxonomy-embl-heidelberg","embl_taxonomy-kreshuk-group","embl_taxonomy-prevedel-group"],"acf":{"featured":true,"show_featured_image":false,"color":"#007B53","link_color":"#fff","article_intro":"
Machine learning helps some of the best microscopes to see better, work faster, and process more data<\/p>\n","related_links":[{"link_description":"Kreshuk group","link_url":"https:\/\/www.embl.org\/groups\/kreshuk\/"},{"link_description":"Prevedel group","link_url":"https:\/\/www.embl.org\/groups\/prevedel\/"}],"article_sources":[{"source_description":"
Wagner, N., et al. Deep learning-enhanced light-field imaging with continuous validation. Nature Methods<\/em>. Published on 7 May 2021. DOI: 0.1038\/s41592-021-01136-0<\/p>\n","source_link_url":"https:\/\/dx.doi.org\/10.1038\/s41592-021-01136-0"}],"in_this_article":false,"youtube_url":"","mp4_url":"","video_caption":"","press_contact":"None","vf_locked":false,"source_article":false,"field_target_display":""},"embl_taxonomy_terms":[{"uuid":"a:3:{i:0;s:36:\"b14d3f13-5670-44fb-8970-e54dfd9c921a\";i:1;s:36:\"89e00fee-87f4-482e-a801-4c3548bb6a58\";i:2;s:36:\"ab46b6d4-71d8-49f8-b2f4-b326d4c8ea4e\";}","parents":[],"name":["EMBL Heidelberg"],"slug":"embl-heidelberg","description":"Where > All EMBL sites > EMBL Heidelberg"},{"uuid":"a:3:{i:0;s:36:\"302cfdf7-365b-462a-be65-82c7b783ebf7\";i:1;s:36:\"64999cc4-9a7c-4fea-8339-0e2acc990e08\";i:2;s:36:\"f97d9bce-0a98-4250-ab74-de726b969114\";}","parents":[],"name":["Kreshuk Group"],"slug":"kreshuk-group","description":"What > Cell biology and biophysics > Kreshuk 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