{"id":15033,"date":"2024-01-18T10:19:04","date_gmt":"2024-01-18T09:19:04","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&#038;p=15033"},"modified":"2024-02-28T17:23:04","modified_gmt":"2024-02-28T16:23:04","slug":"style-transfer-between-microscopy-and-magnetic-resonance-imaging-via-generative-adversarial-network-in-small-sample-size-settings","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/style-transfer-between-microscopy-and-magnetic-resonance-imaging-via-generative-adversarial-network-in-small-sample-size-settings\/","title":{"rendered":"Style transfer between microscopy and magnetic resonance imaging via generative adversarial network in small sample size settings"},"content":{"rendered":"\n<h2 class=\"wp-block-heading eplus-wrapper\">Monika Pytlarz, Adrian Onicas, Alessandro Crimi<\/h2>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising because it can allow histopathological analysis in the absence of an underlying invasive biopsy procedure. Here, we tested a method for generating microscopic histological images from MRI scans of the corpus callosum using conditional generative adversarial network (cGAN) architecture. To our knowledge, this is the first multimodal translation of the brain MRI to histological volumetric representation of the same sample. The technique was assessed by training paired image translation models taking sets of images from MRI scans and microscopy. The use of cGAN for this purpose is challenging because microscopy images are large in size and typically have low sample availability. The current work demonstrates that the framework reliably synthesizes histology images from MRI scans of corpus callosum, emphasizing the network&#8217;s ability to train on high resolution histologies paired with relatively lower-resolution MRI scans. With the ultimate goal of avoiding biopsies, the proposed tool can be used for educational purposes.<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n\t\n    \n        \n\t\t\t<a href=\"https:\/\/arxiv.org\/abs\/2310.10414\" target=\"_blank\" rel= \"noopener noreferrer nofollow\" class=\"button primary \">\n\n\t\t\t\t<span>\n\t\t\t\t\tREAD MORE\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n        \n    \n","protected":false},"excerpt":{"rendered":"<p>In: IEEE International Conference on Image Processing (ICIP), Kuala Lumpur, Malaysia, 2023.<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[15],"class_list":["post-15033","research","type-research","status-publish","hentry","research_type-publications","research_team-computational-neuroscience"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Style transfer between microscopy and magnetic resonance imaging via generative adversarial network in small sample size settings - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/research\/style-transfer-between-microscopy-and-magnetic-resonance-imaging-via-generative-adversarial-network-in-small-sample-size-settings\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Style transfer between microscopy and magnetic resonance imaging via generative adversarial network in small sample size settings\" \/>\n<meta property=\"og:description\" content=\"In: IEEE International Conference on Image Processing (ICIP), Kuala Lumpur, Malaysia, 2023.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/research\/style-transfer-between-microscopy-and-magnetic-resonance-imaging-via-generative-adversarial-network-in-small-sample-size-settings\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2024-02-28T16:23:04+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/style-transfer-between-microscopy-and-magnetic-resonance-imaging-via-generative-adversarial-network-in-small-sample-size-settings\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/research\\\/style-transfer-between-microscopy-and-magnetic-resonance-imaging-via-generative-adversarial-network-in-small-sample-size-settings\\\/\",\"name\":\"Style transfer between microscopy and magnetic resonance imaging via generative adversarial network in small sample size settings - 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