Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations

<!DOCTYPE html>     <html class="no-js" lang="en">  <head> <title>Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations - NASA/ADS</title>  <link rel="apple-touch-icon" sizes="180x180" href="//styles/favicon/apple-touch-icon.png" /> <link rel="icon" type="image/png" sizes="32x32" href="//styles/favicon/favicon-32x32.png" /> <link rel="icon" type="image/png" sizes="16x16" href="//styles/favicon/favicon-16x16.png" /> <link rel="manifest" href="//styles/favicon/site.webmanifest" /> <link rel="mask-icon" href="//styles/favicon/safari-pinned-tab.svg" color="#5bbad5" /> <meta name="apple-mobile-web-app-title" content="NASA ADS" /> <meta name="application-name" content="NASA ADS" /> <meta name="msapplication-TileColor" content="#ffc40d" /> <meta name="theme-color" content="#ffffff" />  <link rel="stylesheet" href="/styles/css/styles.css"> <meta name="robots" content="noarchive"> <link rel="canonical" href="http://ui.adsabs.harvard.edu/abs/2023arXiv230610941K/abstract"/> <meta name="description" content="Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio. The use of modern, deep learning-based segmentation methods has been inhibited by a lack of large datasets with detailed annotations of the blood vessels. To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data. However, the previously proposed simulations fail to faithfully model the retinal vasculature and do not provide effective domain adaptation. Because of this, current methods are unable to fully segment the retinal vasculature, in particular the smallest capillaries. In this work, we present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We then introduce three contrast adaptation pipelines to decrease the domain gap between real and artificial images. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets that compare our method to traditional computer vision algorithms and supervised training using human annotations. Finally, we make our entire pipeline publicly available, including the source code, pretrained models, and a large dataset of synthetic OCTA images.">  <meta property="og:type" content="eprint"> <meta property="og:title" content="Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations"> <meta property="og:site_name" content="NASA/ADS"> <meta property="og:description" content="Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio. The use of modern, deep learning-based segmentation methods has been inhibited by a lack of large datasets with detailed annotations of the blood vessels. To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data. However, the previously proposed simulations fail to faithfully model the retinal vasculature and do not provide effective domain adaptation. Because of this, current methods are unable to fully segment the retinal vasculature, in particular the smallest capillaries. In this work, we present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We then introduce three contrast adaptation pipelines to decrease the domain gap between real and artificial images. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets that compare our method to traditional computer vision algorithms and supervised training using human annotations. Finally, we make our entire pipeline publicly available, including the source code, pretrained models, and a large dataset of synthetic OCTA images."> <meta property="og:url" content="https://ui.adsabs.harvard.edu/abs/2023arXiv230610941K/abstract"> <meta property="og:image" content="https://ui.adsabs.harvard.edu/styles/img/transparent_logo.svg"> <meta property="article:published_time" content="06/2023"> <meta property="article:author" content="Kreitner, Linus"> <meta property="article:author" content="Paetzold, Johannes C."> <meta property="article:author" content="Rauch, Nikolaus"> <meta property="article:author" content="Chen, Chen"> <meta property="article:author" content="Hagag, Ahmed M."> <meta property="article:author" content="Fayed, Alaa E."> <meta property="article:author" content="Sivaprasad, Sobha"> <meta property="article:author" content="Rausch, Sebastian"> <meta property="article:author" content="Weichsel, Julian"> <meta property="article:author" content="Menze, Bjoern H."> <meta property="article:author" content="Harders, Matthias"> <meta property="article:author" content="Knier, Benjamin"> <meta property="article:author" content="Rueckert, Daniel"> <meta property="article:author" content="Menten, Martin J.">  <meta name="citation_journal_title" content="arXiv e-prints"> <meta name="citation_authors" content="Kreitner, Linus;Paetzold, Johannes C.;Rauch, Nikolaus;Chen, Chen;Hagag, Ahmed M.;Fayed, Alaa E.;Sivaprasad, Sobha;Rausch, Sebastian;Weichsel, Julian;Menze, Bjoern H.;Harders, Matthias;Knier, Benjamin;Rueckert, Daniel;Menten, Martin J."> <meta name="citation_title" content="Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations"> <meta name="citation_date" content="06/2023"> <meta name="citation_firstpage" content="arXiv:2306.10941"> <meta name="citation_doi" content="10.48550/arXiv.2306.10941"> <meta name="citation_language" content="en"> <meta name="citation_keywords" content="Electrical Engineering and Systems Science - Image and Video Processing"> <meta name="citation_keywords" content="Computer Science - Computer Vision and Pattern Recognition"> <meta name="citation_abstract_html_url" content="https://ui.adsabs.harvard.edu/abs/2023arXiv230610941K/abstract"> <meta name="citation_publication_date" content="06/2023"> <meta name="citation_arxiv_id" content="arXiv:2306.10941" /> <link title="schema(PRISM)" rel="schema.prism" href="http://prismstandard.org/namespaces/1.2/basic/" /> <meta name="prism.publicationDate" content="06/2023" /> <meta name="prism.publicationName" content="arXiv" /> <meta name="prism.startingPage" content="arXiv:2306.10941" /> <link title="schema(DC)" rel="schema.dc" href="http://purl.org/dc/elements/1.1/" /> <meta name="dc.identifier" content="doi:10.48550/arXiv.2306.10941" /> <meta name="dc.date" content="06/2023" /> <meta name="dc.source" content="arXiv" /> <meta name="dc.title" content="Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations" /> <meta name="dc.creator" content="Kreitner, Linus"> <meta name="dc.creator" content="Paetzold, Johannes C."> <meta name="dc.creator" content="Rauch, Nikolaus"> <meta name="dc.creator" content="Chen, Chen"> <meta name="dc.creator" content="Hagag, Ahmed M."> <meta name="dc.creator" content="Fayed, Alaa E."> <meta name="dc.creator" content="Sivaprasad, Sobha"> <meta name="dc.creator" content="Rausch, Sebastian"> <meta name="dc.creator" content="Weichsel, Julian"> <meta name="dc.creator" content="Menze, Bjoern H."> <meta name="dc.creator" content="Harders, Matthias"> <meta name="dc.creator" content="Knier, Benjamin"> <meta name="dc.creator" content="Rueckert, Daniel"> <meta name="dc.creator" content="Menten, Martin J.">  <meta name="twitter:card" content="summary_large_image"/> <meta name="twitter:description" content="Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio. The use of modern, deep learning-based segmentation methods has been inhibited by a lack of large datasets with detailed annotations of the blood vessels. To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data. However, the previously proposed simulations fail to faithfully model the retinal vasculature and do not provide effective domain adaptation. Because of this, current methods are unable to fully segment the retinal vasculature, in particular the smallest capillaries. In this work, we present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We then introduce three contrast adaptation pipelines to decrease the domain gap between real and artificial images. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets that compare our method to traditional computer vision algorithms and supervised training using human annotations. Finally, we make our entire pipeline publicly available, including the source code, pretrained models, and a large dataset of synthetic OCTA images."/> <meta name="twitter:title" content="Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations"/> <meta name="twitter:site" content="@adsabs"/> <meta name="twitter:domain" content="NASA/ADS"/> <meta name="twitter:image:src" content="https://ui.adsabs.harvard.edu/styles/img/transparent_logo.svg"/> <meta name="twitter:creator" content="@adsabs"/> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <base href="/"> <style> .btn-full-ads { color: #fff !important; background-color: #1a1a1a !important; border-color: #1a1a1a !important; margin-top: 9px !important; padding-bottom: 10px !important; padding-top: 10px !important; } .btn-full-ads:hover, .btn-full-ads:focus, .btn-full-ads:active, .btn-full-ads.active, 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// Add class "autocomplete-active": x[currentFocus].classList.add("autocomplete-active"); } function removeActive(x) { // Remove the "active" class from all autocomplete items: for (var i = 0; i < x.length; i++) { x[i].classList.remove("autocomplete-active"); } } function closeAllLists(elmnt) { // Close all autocomplete lists in the document, except the one passed as an argument: var x = document.getElementsByClassName("autocomplete-items"); for (var i = 0; i < x.length; i++) { if (elmnt != x[i] && elmnt != searchBox) { x[i].parentNode.removeChild(x[i]); } } } // Any other clicks in the document: document.addEventListener("click", function (e) { closeAllLists(e.target); }); } var autoList = [ { value: 'author:""', label: 'Author', match: 'author:"' }, { value: 'author:"^"', label: 'First Author', match: 'first author' }, { value: 'author:"^"', label: 'First Author', match: 'author:"^' }, { value: 'bibcode:""', label: 'Bibcode', desc: 'e.g. bibcode:1989ApJ...342L..71R', match: 'bibcode:"' }, { value: 'bibstem:""', label: 'Publication', desc: 'e.g. bibstem:ApJ', match: 'bibstem:"' }, { value: 'bibstem:""', label: 'Publication', desc: 'e.g. bibstem:ApJ', match: 'publication (bibstem)' }, { value: 'arXiv:', label: 'arXiv ID', match: 'arxiv:' }, { value: 'doi:', label: 'DOI', match: 'doi:' }, { value: 'full:""', label: 'Full text search', desc: 'title, abstract, and body', match: 'full:' }, { value: 'full:""', label: 'Full text search', desc: 'title, abstract, and body', match: 'fulltext' }, { value: 'full:""', label: 'Full text search', desc: 'title, abstract, and body', match: 'text' }, { value: 'year:', label: 'Year', match: 'year' }, { value: 'year:1999-2005', label: 'Year Range', desc: 'e.g. 1999-2005', match: 'year range' }, { value: 'aff:""', label: 'Affiliation', match: 'aff:' }, { value: 'abs:""', label: 'Search abstract + title + keywords', match: 'abs:' }, { value: 'database:astronomy', label: 'Limit to papers in the astronomy database', match: 'database:astronomy' }, { value: 'database:physics', label: 'Limit to papers in the physics database', match: 'database:physics' }, { value: 'title:""', label: 'Title', match: 'title:"' }, { value: 'orcid:', label: 'ORCiD identifier', match: 'orcid:' }, { value: 'object:', label: 'SIMBAD object (e.g. object:LMC)', match: 'object:' }, { value: 'property:refereed', label: 'Limit to refereed', desc: '(property:refereed)', match: 'refereed' }, { value: 'property:refereed', label: 'Limit to refereed', desc: '(property:refereed)', match: 'property:refereed' }, { value: 'property:notrefereed', label: 'Limit to non-refereed', desc: '(property:notrefereed)', match: 'property:notrefereed' }, { value: 'property:notrefereed', label: 'Limit to non-refereed', desc: '(property:notrefereed)', match: 'notrefereed' }, { value: 'property:eprint', label: 'Limit to eprints', desc: '(property:eprint)', match: 'eprint' }, { value: 'property:eprint', label: 'Limit to eprints', desc: '(property:eprint)', match: 'property:eprint' }, { value: 'property:openaccess', label: 'Limit to open access', desc: '(property:openaccess)', match: 'property:openaccess' }, { value: 'property:openaccess', label: 'Limit to open access', desc: '(property:openaccess)', match: 'openaccess' }, { value: 'doctype:software', label: 'Limit to software', desc: '(doctype:software)', match: 'software' }, { value: 'doctype:software', label: 'Limit to software', desc: '(doctype:software)', match: 'doctype:software' }, { value: 'property:inproceedings', label: 'Limit to papers in conference proceedings', desc: '(property:inproceedings)', match: 'proceedings' }, { value: 'property:inproceedings', label: 'Limit to papers in conference proceedings', desc: '(property:inproceedings)', match: 'property:inproceedings' }, { value: 'citations()', label: 'Citations', desc: 'Get papers citing your search result set', match: 'citations(' }, { value: 'references()', label: 'References', desc: 'Get papers referenced by your search result set', match: 'references(' }, { value: 'trending()', label: 'Trending', desc: 'Get papers most read by users who recently read your search result set', match: 'trending(' }, { value: 'reviews()', label: 'Review Articles', desc: 'Get most relevant papers that cite your search result set', match: 'reviews(' }, { value: 'useful()', label: 'Useful', desc: 'Get papers most frequently cited by your search result set', match: 'useful(' }, { value: 'similar()', label: 'Similar', desc: 'Get papers that have similar full text to your search result set', match: 'similar(' }, ]; // initiate the autocomplete function on the "q" element, and pass along the operators array as possible autocomplete values: inputBox = document.getElementById("q") if (inputBox) { inputBox.focus() // autofucs inputBox.setSelectionRange(inputBox.value.length, inputBox.value.length); // bring cursor to the end autocomplete(inputBox, autoList); } </script> <script> (function() { // turn off no-js if we have javascript document.documentElement.className = document.documentElement.className.replace("no-js", "js"); function getCookie(cname) { var name = cname + "="; var decodedCookie = decodeURIComponent(document.cookie); var ca = decodedCookie.split(';'); for (var i = 0; i < ca.length; i++) { var c = ca[i]; while (c.charAt(0) == ' ') { c = c.substring(1); } if (c.indexOf(name) == 0) { return c.substring(name.length, c.length); } } return ""; } (function() { // looks for the cookie, and sets true if its 'always' const coreCookie = getCookie('core') === 'always'; // only load bumblebee if we detect the core cookie and we are on abstract page if (coreCookie || (!(/^\/abs\//.test(document.location.pathname)) && !coreCookie)) { return; 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Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations - NASA/ADS