{"id":41742,"date":"2021-09-10T11:00:00","date_gmt":"2021-09-10T09:00:00","guid":{"rendered":"https:\/\/www.embl.org\/news\/?p=41742"},"modified":"2024-03-22T11:27:21","modified_gmt":"2024-03-22T10:27:21","slug":"artificial-neural-networks-revolutionise-biological-image-analysis","status":"publish","type":"post","link":"https:\/\/www.embl.org\/news\/science\/artificial-neural-networks-revolutionise-biological-image-analysis\/","title":{"rendered":"Artificial neural networks revolutionise biological image analysis"},"content":{"rendered":"\n<p>Scientists use super-resolution microscopy to study previously undiscovered cellular worlds, revealing nanometre-scale details inside cells. The method revolutionised light microscopy and earned its inventors the 2014 Nobel Prize in Chemistry.<\/p>\n\n\n\n<p>Single-molecule localisation microscopy (SMLM) is a type of super-resolution microscopy. It involves labelling proteins of interest with fluorescent molecules and using light to activate only a few molecules at a time. Using this method, multiple images of the same sample are acquired. To create a meaningful picture, a computer program unscrambles the data and compiles the complete image.<\/p>\n\n\n\n<p>While the technique can be used to locate molecules with high precision, it requires scientists to acquire a large number of images. \u201cOne of the biggest limitations of super-resolution microscopy is how long it takes to collect data from the microscope,\u201d explained Lucas-Raphael M\u00fcller, a former master&#8217;s degree student in the Ries Group at EMBL Heidelberg.<\/p>\n\n\n\n<p>In a collaborative effort, the Ries Group, together with the labs of Professor Jakob Macke at the University of T\u00fcbingen (<a href=\"https:\/\/uni-tuebingen.de\/en\/research\/core-research\/cluster-of-excellence-machine-learning\/home\/\">Cluster of Excellence \u2013 Machine Learning for Science<\/a>) and Dr Srinivas Turaga at <a href=\"https:\/\/www.janelia.org\/\">Janelia Research Campus<\/a>, developed a new algorithm that overcomes several limitations of SMLM.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Deep learning enables fast and highly accurate single-molecule localisation<\/strong><\/h2>\n\n\n\n<p>DECODE (<a href=\"https:\/\/github.com\/TuragaLab\/DECODE\">DEep COntext DEpendent<\/a>) is a computer program based on a neural network that learns from training data and gets better over time as it is fed more and more examples. The scientists used a trick that was recently established for training neural networks: instead of using real images to train the network, they used synthetic data generated by a numerical simulation. By incorporating information about the microscopic setup and the imaging physics, the researchers achieved simulations that closely matched real-world acquisitions. As a result, a neural network that is trained to localise fluorescence molecules using simulated data can also localise fluorophores in real images.<\/p>\n\n\n\n<p>One of the main benefits of DECODE is that it accurately detects and localises fluorophores at higher densities than were previously possible. This means that researchers can activate more fluorophores for imaging in a single camera frame, and fewer images are needed per sample. As a result, imaging speeds can be increased up to tenfold with minimal loss of resolution.<\/p>\n\n\n\n<p>\u201cInstead of looking at frames in isolation, like previous computer programs for super-resolution microscopes, DECODE uses information from the neighbouring frames to get more context,\u201d explained Artur Speiser, a PhD student in the Macke and Turaga labs. In a single image, several fluorescing spots that are close together might look like one molecule. But looking at previous and next images of the same area can reveal that parts of this fluorescent glow switch off at different times, which suggests that they are distinct entities.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Interdisciplinary research expands possibilities in SMLM research<\/strong><\/h2>\n\n\n\n<p>\u201cThis novel approach is more efficient for imaging activity in living cells, and for studying fragile samples that can break down under extended exposure to harsh light,\u201d said Dr Turaga. \u201cAnd it is possible to upgrade existing analysis software using DECODE, which will allow many labs performing SMLM to improve their reconstructions, even on existing datasets.\u201d<\/p>\n\n\n\n<p>The artificial neural network DECODE software thus solves several limitations of SMLM. \u201cThese research results could be achieved only by the excellent collaboration between the different groups with interdisciplinary backgrounds,\u201d added EMBL Group Leader Jonas Ries. \u201cThe software is simple to install and free to use, so we hope it will be useful for many scientists in the future.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Packaged for simple installation and free use, the novel method DECODE enables researchers to reduce imaging times and increase localisation density in single-molecule localisation microscopy (SMLM).<\/p>\n","protected":false},"author":114,"featured_media":42128,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[2,17591],"tags":[5696,474,1012,604,79,11046],"embl_taxonomy":[19363],"class_list":["post-41742","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-science","category-science-technology","tag-advanced-light-microscopy","tag-collaboration","tag-fluorescence-microscopy","tag-machine-learning","tag-microscopy","tag-single-molecule-localization-microscopy","embl_taxonomy-ries-group-visiting"],"acf":{"featured":true,"show_featured_image":false,"article_intro":"<p>EMBL scientists and their collaborators have developed DECODE, a neural network based fitter that accelerates super-resolution microscopy imaging <\/p>\n","related_links":[{"link_description":"DECODE Online","link_url":"https:\/\/github.com\/TuragaLab\/DECODE"},{"link_description":"EMBL Ries Group","link_url":"https:\/\/www.embl.org\/groups\/ries\/"},{"link_description":"University of T\u00fcbingen","link_url":"https:\/\/uni-tuebingen.de\/en\/"},{"link_description":"Janelia Research Campus, Virginia, USA","link_url":"https:\/\/www.janelia.org\/"}],"source_article":[{"publication_title":"Deep learning enables fast and dense single-molecule localization with high accuracy","publication_link":{"title":"Deep learning enables fast and dense single-molecule localization with high accuracy","url":"https:\/\/www.nature.com\/articles\/s41592-021-01236-x?proof=thttps%3A%2F%2Fwww.nature.com%2Farticles%2Fsj.bdj.2014.353%3Fproof%3Dt","target":""},"publication_authors":"Speiser A., M\u00fcller LR., et al.","publication_source":"Nature Methods","publication_date":"03 September 2021","publication_doi":"10.1038\/s41592-021-01236-x"}],"in_this_article":false,"press_contact":"None","vf_locked":false},"embl_taxonomy_terms":[{"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:\"685f39f1-defb-4bd8-bad0-ef386025ee87\";}","parents":[],"name":["Ries Group (Visiting)"],"slug":"ries-group-visiting","description":"What &gt; Cell biology and biophysics &gt; Ries Group (Visiting)","deprecated":true}],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Artificial neural networks revolutionise biological image analysis | EMBL<\/title>\n<meta name=\"description\" content=\"With the deep-learning DECODE method, collaborating scientists have overcome limitations of localisation-based super-resolution microscopy.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.embl.org\/news\/science\/artificial-neural-networks-revolutionise-biological-image-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Artificial neural networks revolutionise biological image analysis | EMBL\" \/>\n<meta property=\"og:description\" content=\"With the deep-learning DECODE method, collaborating scientists have overcome limitations of localisation-based super-resolution microscopy.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.embl.org\/news\/science\/artificial-neural-networks-revolutionise-biological-image-analysis\/\" \/>\n<meta property=\"og:site_name\" content=\"EMBL\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/embl.org\/\" \/>\n<meta property=\"article:published_time\" content=\"2021-09-10T09:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-03-22T10:27:21+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.embl.org\/news\/wp-content\/uploads\/2021\/09\/Labelled-Microtubules-in-single-molecule-localization-microscopy.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta property=\"og:image:height\" content=\"600\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Lisa Vollmar\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@embl\" \/>\n<meta name=\"twitter:site\" content=\"@embl\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Lisa Vollmar\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"NewsArticle\",\"@id\":\"https:\/\/www.embl.org\/news\/science\/artificial-neural-networks-revolutionise-biological-image-analysis\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.embl.org\/news\/science\/artificial-neural-networks-revolutionise-biological-image-analysis\/\"},\"author\":{\"name\":\"Lisa Vollmar\",\"@id\":\"https:\/\/www.embl.org\/news\/#\/schema\/person\/cdb35218b2c7e9143b216cb18c74d6c6\"},\"headline\":\"Artificial neural networks revolutionise biological image analysis\",\"datePublished\":\"2021-09-10T09:00:00+00:00\",\"dateModified\":\"2024-03-22T10:27:21+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.embl.org\/news\/science\/artificial-neural-networks-revolutionise-biological-image-analysis\/\"},\"wordCount\":578,\"publisher\":{\"@id\":\"https:\/\/www.embl.org\/news\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.embl.org\/news\/science\/artificial-neural-networks-revolutionise-biological-image-analysis\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.embl.org\/news\/wp-content\/uploads\/2021\/09\/Labelled-Microtubules-in-single-molecule-localization-microscopy.png\",\"keywords\":[\"advanced light microscopy\",\"collaboration\",\"fluorescence microscopy\",\"machine learning\",\"microscopy\",\"single-molecule localization microscopy\"],\"articleSection\":[\"Science\",\"Science &amp; 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