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<!DOCTYPE html> <html lang="ko"> <head> <title>OASIS Repository@POSTECHLIBRARY: Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training</title> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /> <meta name="Generator" content="DSpace 5.5" /> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="description" content="Postech OASIS Repository" /> <meta name="keywords" content="오아시스,레포지토리,리포지터리,저장소,포스텍,박태준학술정보관,포항공과대학교,포항공대,POSTECH,Pohang University of Science and Technology,도서관,IR,연구업적,학술실적,연구성과,dspace" /> <link rel="shortcut icon" href="/favicon.ico" type="image/x-icon"/> <link rel="search" type="application/opensearchdescription+xml" href="/open-search/description.xml" title="DSpace" /> <link rel="schema.DCTERMS" href="http://purl.org/dc/terms/" /> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <link rel="schema.OAK" href="http://www.oak.go.kr/terms/" /> <meta name="OAK.author" content="Lyu, Ilwoo" scheme="OAK.AUTHOR" /> <meta name="OAK.author" content="Bao, Shunxing" scheme="OAK.AUTHOR" /> <meta name="OAK.author" content="Hao, Lingyan" scheme="OAK.AUTHOR" /> <meta name="OAK.author" content="Yao, Jewelia" scheme="OAK.AUTHOR" /> <meta name="OAK.author" content="Miller, Jacob A." scheme="OAK.AUTHOR" /> <meta name="OAK.author" content="Voorhies, Willa" scheme="OAK.AUTHOR" /> <meta name="OAK.author" content="Taylor, Warren D." scheme="OAK.AUTHOR" /> <meta name="OAK.author" content="Bunge, Silvia A." scheme="OAK.AUTHOR" /> <meta name="OAK.author" content="Weiner, Kevin S." scheme="OAK.AUTHOR" /> <meta name="OAK.author" content="Landman, Bennett A." scheme="OAK.AUTHOR" /> <meta name="DCTERMS.dateAccepted" content="2024-03-05T04:50:21Z" scheme="DCTERMS.W3CDTF" /> <meta name="DCTERMS.available" content="2024-03-05T04:50:21Z" scheme="DCTERMS.W3CDTF" /> <meta name="DCTERMS.created" content="2024-03-04" scheme="DCTERMS.W3CDTF" /> <meta name="DCTERMS.issued" content="2021-04" scheme="DCTERMS.W3CDTF" /> <meta name="OAK.identifier.issn" content="1053-8119" scheme="OAK.ISSN" /> <meta name="DC.identifier" content="https://oasis.postech.ac.kr/handle/2014.oak/120862" scheme="DCTERMS.URI" /> <meta name="DCTERMS.abstract" content="The inference of cortical sulcal labels often focuses on deep (primary and secondary) sulcal regions, whereas shallow (tertiary) sulcal regions are largely overlooked in the literature due to the scarcity of manual/well-defined annotations and their large neuroanatomical variability. In this paper, we present an automated framework for regional labeling of both primary/secondary and tertiary sulci of the dorsal portion of lateral prefrontal cortex (LPFC) using spherical convolutional neural networks. We propose two core components that enhance the inference of sulcal labels to overcome such large neuroanatomical variability: (1) surface data augmentation and (2) context-aware training. (1) To take into account neuroanatomical variability, we synthesize training data from the proposed feature space that embeds intermediate deformation trajectories of spherical data in a rigid to non-rigid fashion, which bridges an augmentation gap in conventional rotation data augmentation. (2) Moreover, we design a two-stage training process to improve labeling accuracy of tertiary sulci by informing the biological associations in neuroanatomy: inference of primary/secondary sulci and then their spatial likelihood to guide the definition of tertiary sulci. In the experiments, we evaluate our method on 13 deep and shallow sulci of human LPFC in two independent data sets with different age ranges: pediatric (N=60) and adult (N=36) cohorts. We compare the proposed method with a conventional multi-atlas approach and spherical convolutional neural networks without/with rotation data augmentation. In both cohorts, the proposed data augmentation improves labeling accuracy of deep and shallow sulci over the baselines, and the proposed context-aware training offers further improvement in the labeling of shallow sulci over the proposed data augmentation. We share our tools with the field and discuss applications of our results for understanding neuroanatomical-functional organization of LPFC and the rest of cortex (https://github.com/ilwoolyu/SphericalLabeling)." /> <meta name="DC.language" content="English" /> <meta name="DC.publisher" content="Academic Press" /> <meta name="DC.relation" content="NeuroImage" /> <meta name="DC.title" content="Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training" /> <meta name="DC.type" content="Article" /> <meta name="DC.identifier" content="10.1016/j.neuroimage.2021.117758" /> <meta name="DC.identifier" content="57158" /> <meta name="DC.type" content="ART" /> <meta name="DC.identifier" content="NeuroImage, v.229, pp.117758" /> <meta name="DC.identifier" content="000629509400037" /> <meta name="OAK.relation.page" content="117758" scheme="OAK.PAGE" /> <meta name="OAK.relation.journal" content="NeuroImage" scheme="OAK.JOURNAL" /> <meta name="OAK.relation.volume" content="229" scheme="OAK.VOLUME" /> <meta name="DC.contributor" content="Lyu, Ilwoo" /> <meta name="DC.identifier" content="2-s2.0-85100029593" /> <meta name="DC.description" content="1" /> <meta name="DC.description" content="1" /> <meta name="DC.description" content="Y" /> <meta name="DC.type" content="Article; 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In this paper, we present an automated framework for regional labeling of both primary/secondary and tertiary sulci of the dorsal portion of lateral prefrontal cortex (LPFC) using spherical convolutional neural networks. We propose two core components that enhance the inference of sulcal labels to overcome such large neuroanatomical variability: (1) surface data augmentation and (2) context-aware training. (1) To take into account neuroanatomical variability, we synthesize training data from the proposed feature space that embeds intermediate deformation trajectories of spherical data in a rigid to non-rigid fashion, which bridges an augmentation gap in conventional rotation data augmentation. (2) Moreover, we design a two-stage training process to improve labeling accuracy of tertiary sulci by informing the biological associations in neuroanatomy: inference of primary/secondary sulci and then their spatial likelihood to guide the definition of tertiary sulci. In the experiments, we evaluate our method on 13 deep and shallow sulci of human LPFC in two independent data sets with different age ranges: pediatric (N=60) and adult (N=36) cohorts. We compare the proposed method with a conventional multi-atlas approach and spherical convolutional neural networks without/with rotation data augmentation. In both cohorts, the proposed data augmentation improves labeling accuracy of deep and shallow sulci over the baselines, and the proposed context-aware training offers further improvement in the labeling of shallow sulci over the proposed data augmentation. We share our tools with the field and discuss applications of our results for understanding neuroanatomical-functional organization of LPFC and the rest of cortex (https://github.com/ilwoolyu/SphericalLabeling).</dd></dl> <dl><dt>URI</dt><dd><a href="https://oasis.postech.ac.kr/handle/2014.oak/120862" class="link_type">https://oasis.postech.ac.kr/handle/2014.oak/120862</a></dd></dl> <dl><dt>DOI</dt><dd><a href="http://dx.doi.org/10.1016/j.neuroimage.2021.117758">10.1016/j.neuroimage.2021.117758</a></dd></dl> <dl><dt>ISSN</dt><dd>1053-8119</dd></dl> <dl><dt>Article Type</dt><dd>Article</dd></dl> <dl><dt>Citation</dt><dd>NeuroImage, vol. 229, page. 117758, 2021-04</dd></dl> <dl class="file_item_dl"><dt>Files in This Item:</dt> <dd class="file_download">There are no files associated with this item.</dd> </dl> </div> <div class="record_bt_box"> <a href="/handle/2014.oak/120862?mode=full">Show full item record</a> </div> <div class="al_right"> </div> <div class="sns_wrap"> <div class="sns_box"> <p class="qr_box"><img src="https://api.qrserver.com/v1/create-qr-code/?size=66x66&data=https://oasis.postech.ac.kr/handle/2014.oak/120862" alt="qr_code"></p> <div class="sns_inner"> <ul> <li> <p><a href="http://www.mendeley.com/import/?url=https://oasis.postech.ac.kr/handle/2014.oak/120862" target="_blank"><img src="/image/common/mendeley_icon.gif" alt="mendeley" /></a></p> <p class="twitter_box"> <a href="https://twitter.com/share" class="tweet_bt twitter-share-button" data-lang="en" data-size="large" data-dnt="true">트윗하기</a> <script> !function(d, s, id) { var js, fjs = d.getElementsByTagName(s)[0]; if (!d.getElementById(id)) { js = d.createElement(s); js.id = id; js.src = "//platform.twitter.com/widgets.js"; fjs.parentNode.insertBefore(js, fjs); } }(document, "script", "twitter-wjs"); </script> </p> </li> <li class="facebook_li"> <span id="fb-root"></span> <script> (function(d, s, id) { var js, fjs = d.getElementsByTagName(s)[0]; if (d.getElementById(id)) return; js = d.createElement(s); js.id = id; js.src = "//connect.facebook.net/ko_KR/all.js#xfbml=1"; fjs.parentNode.insertBefore(js, fjs); }(document, 'script', 'facebook-jssdk')); </script> <span class="fb-like" data-send="true" data-layout="standard" data-width="450" data-show-faces="false" data-font="verdana"></span> </li> </ul> </div> </div> <p class="sns_text"> <span> Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.</span> </p> </div> </div> </div>  <div class="sub_right_box"> <div class="w_cc"> <h4 class="widget_title">Communities & Collection</h4> <ul> <li><a href="/handle/2014.oak/110943" class="cc_item">Graduate School of Artificial Intelligence (인공지능대학원)</a> <ul> <li><a href="/handle/2014.oak/110945">1. 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