{"id":13988,"date":"2023-10-02T21:03:37","date_gmt":"2023-10-02T19:03:37","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&p=13988"},"modified":"2024-01-05T13:37:55","modified_gmt":"2024-01-05T12:37:55","slug":"deep-learning-for-endoscopic-procedures","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/deep-learning-for-endoscopic-procedures\/","title":{"rendered":"Deep learning for Endoscopic Procedures"},"content":{"rendered":"\n

Joanna Kaleta<\/h2>\n\n\n\n
<\/div>\n\n\n\n

Endoscopic surgeries allow surgeons to operate with minimal incisions and significantly reduce patient trauma. We focus on applying deep learning to improve surgical outcomes and advance the capabilities of these procedures.<\/p>\n\n\n\n

<\/div>\n\n\n\n
(1) Bridging the Gap between Data and Computer-Assisted Surgery:<\/strong><\/h5>\n\n\n\n
<\/div>\n\n\n\n

Computer-assisted surgical systems have the potential to revolutionize surgical practices, offering surgeons real-time guidance and insights that can transform the outcomes of complex procedures. These systems rely on deep learning models that are trained on diverse and intricate datasets. However, the challenge lies in the limited availability of annotated data for training these models effectively. Generating synthetic data can overcome these limitations, but it is necessary to reduce the domain gap between real and synthetic data. We combine simulator data and generative learning methods to generate realistic fully labelled datasets.<\/p>\n\n\n\n

<\/div>\n\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n

Figure 1. We utilise the Stable Diffusion model to transfer synthetic endoscopic data from a simulator into realistic images. Source: own elaboration.<\/p>\n\n\n\n

<\/div>\n\n\n\n
(2) Advanced Visualization Tools for Fetoscopic Surgery<\/strong>:<\/h5>\n\n\n\n
<\/div>\n\n\n\n

Fetoscopy laser photocoagulation is a widely used procedure for the treatment of Twin-to-Twin Transfusion Syndrome (TTTS), that occurs in multiple pregnancies due to placental vascular anastomoses.  We aim to develop a framework providing better visualization of the placental vessel network for TTTS treatment procedure. Our research focuses on the following areas:  eliminating artefacts and enhancing the quality of endoscopic images, mosaicking, and improving placental vessel segmentation.<\/p>\n\n\n\n

<\/div>\n\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n
<\/div>\n\n\n\n

Figure 2. Example use-cases for supporting visualisation tools: mosaicking (top), inpainting and denoising (bottom). Source: Alabi et al. Robust fetoscopic mosaicking from deep learned flow fields. Int J CARS 17, 1125\u20131134 (2022), own elaboration.<\/p>\n","protected":false},"excerpt":{"rendered":"

Endoscopic surgeries allow surgeons to operate with minimal incisions and significantly reduce patient trauma. <\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[7],"research_team":[17],"class_list":["post-13988","research","type-research","status-publish","hentry","research_type-research-topics","research_team-health-informatics-group-higs"],"yoast_head":"\nDeep learning for Endoscopic Procedures - 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\/deep-learning-for-endoscopic-procedures\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Deep learning for Endoscopic Procedures\" \/>\n<meta property=\"og:description\" content=\"Endoscopic surgeries allow surgeons to operate with minimal incisions and significantly reduce patient trauma.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/research\/deep-learning-for-endoscopic-procedures\/\" \/>\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=\"2024-01-05T12:37:55+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/10\/Bez-tytulu1.png\" \/>\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=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/deep-learning-for-endoscopic-procedures\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/research\\\/deep-learning-for-endoscopic-procedures\\\/\",\"name\":\"Deep learning for Endoscopic Procedures - 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We focus on applying deep learning to improve surgical outcomes and advance the capabilities of these procedures.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Endoscopic surgeries allow surgeons to operate with minimal incisions and significantly reduce patient trauma. We focus on applying deep learning to improve surgical outcomes and advance the capabilities of these procedures.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-sawxMX","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/heading","attrs":{"level":5,"epAnimationGeneratedClass":"edplus_anim-uSSMm0","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<h5 class=\"wp-block-heading eplus-wrapper\"><strong>(1) Bridging the Gap between Data and Computer-Assisted Surgery:<\/strong><\/h5>\n","innerContent":["\n<h5 class=\"wp-block-heading eplus-wrapper\"><strong>(1) Bridging the Gap between Data and Computer-Assisted Surgery:<\/strong><\/h5>\n"]},{"blockName":"core\/spacer","attrs":{"height":"20px","epAnimationGeneratedClass":"edplus_anim-UTuxs0","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-RkNXkh","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Computer-assisted surgical systems have the potential to revolutionize surgical practices, offering surgeons real-time guidance and insights that can transform the outcomes of complex procedures. These systems rely on deep learning models that are trained on diverse and intricate datasets. However, the challenge lies in the limited availability of annotated data for training these models effectively. Generating synthetic data can overcome these limitations, but it is necessary to reduce the domain gap between real and synthetic data. We combine simulator data and generative learning methods to generate realistic fully labelled datasets.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Computer-assisted surgical systems have the potential to revolutionize surgical practices, offering surgeons real-time guidance and insights that can transform the outcomes of complex procedures. These systems rely on deep learning models that are trained on diverse and intricate datasets. However, the challenge lies in the limited availability of annotated data for training these models effectively. Generating synthetic data can overcome these limitations, but it is necessary to reduce the domain gap between real and synthetic data. We combine simulator data and generative learning methods to generate realistic fully labelled datasets.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-n0hjxY","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/image","attrs":{"align":"center","id":13990,"sizeSlug":"full","linkDestination":"none","epAnimationGeneratedClass":"edplus_anim-7RM61g","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<figure class=\"wp-block-image aligncenter size-full eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/10\/Bez-tytulu1.png\" alt=\"\" class=\"wp-image-13990\"\/><\/figure>\n","innerContent":["\n<figure class=\"wp-block-image aligncenter size-full eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/10\/Bez-tytulu1.png\" alt=\"\" class=\"wp-image-13990\"\/><\/figure>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-wCJ9ac","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Figure 1. We utilise the Stable Diffusion model to transfer synthetic endoscopic data from a simulator into realistic images. Source: own elaboration.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Figure 1. We utilise the Stable Diffusion model to transfer synthetic endoscopic data from a simulator into realistic images. Source: own elaboration.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-bbttr5","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/heading","attrs":{"level":5,"epAnimationGeneratedClass":"edplus_anim-X2qLjJ","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<h5 class=\"wp-block-heading eplus-wrapper\"><strong>(2) Advanced Visualization Tools for Fetoscopic Surgery<\/strong>:<\/h5>\n","innerContent":["\n<h5 class=\"wp-block-heading eplus-wrapper\"><strong>(2) Advanced Visualization Tools for Fetoscopic Surgery<\/strong>:<\/h5>\n"]},{"blockName":"core\/spacer","attrs":{"height":"20px","epAnimationGeneratedClass":"edplus_anim-rJhoT9","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-3Tacdg","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Fetoscopy laser photocoagulation is a widely used procedure for the treatment of Twin-to-Twin Transfusion Syndrome (TTTS), that occurs in multiple pregnancies due to placental vascular anastomoses.  We aim to develop a framework providing better visualization of the placental vessel network for TTTS treatment procedure. Our research focuses on the following areas:  eliminating artefacts and enhancing the quality of endoscopic images, mosaicking, and improving placental vessel segmentation.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Fetoscopy laser photocoagulation is a widely used procedure for the treatment of Twin-to-Twin Transfusion Syndrome (TTTS), that occurs in multiple pregnancies due to placental vascular anastomoses.  We aim to develop a framework providing better visualization of the placental vessel network for TTTS treatment procedure. Our research focuses on the following areas:  eliminating artefacts and enhancing the quality of endoscopic images, mosaicking, and improving placental vessel segmentation.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-TzRUjV","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/image","attrs":{"align":"center","id":13998,"sizeSlug":"full","linkDestination":"none","epAnimationGeneratedClass":"edplus_anim-7XBWTi","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<figure class=\"wp-block-image aligncenter size-full eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/10\/Bez-tytulu2.png\" alt=\"\" class=\"wp-image-13998\"\/><\/figure>\n","innerContent":["\n<figure class=\"wp-block-image aligncenter size-full eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/10\/Bez-tytulu2.png\" alt=\"\" class=\"wp-image-13998\"\/><\/figure>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-kECfb3","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-NkbVNX","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Figure 2. Example use-cases for supporting visualisation tools: mosaicking (top), inpainting and denoising (bottom). Source: Alabi et al. Robust fetoscopic mosaicking from deep learned flow fields. Int J CARS 17, 1125\u20131134 (2022), own elaboration.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Figure 2. Example use-cases for supporting visualisation tools: mosaicking (top), inpainting and denoising (bottom). Source: Alabi et al. Robust fetoscopic mosaicking from deep learned flow fields. Int J CARS 17, 1125\u20131134 (2022), own elaboration.<\/p>\n"]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/13988","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":10,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/13988\/revisions"}],"predecessor-version":[{"id":14670,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/13988\/revisions\/14670"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=13988"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=13988"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=13988"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}