{"id":8504,"date":"2012-11-15T22:39:54","date_gmt":"2012-11-15T20:39:54","guid":{"rendered":"http:\/\/hgpu.org\/?p=8504"},"modified":"2012-11-15T22:39:54","modified_gmt":"2012-11-15T20:39:54","slug":"fast-3d-structure-localization-in-medical-volumes-using-cuda-enabled-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8504","title":{"rendered":"Fast 3D Structure Localization in Medical Volumes using CUDA-enabled GPUs"},"content":{"rendered":"<p>Effective and fast localization of anatomical structures is a crucial first step towards automated analysis of medical volumes. In this paper, we propose an iterative approach for structure localization in medical volumes based on the adaptive bandwidth mean-shift algorithm for object detection (ABMSOD). We extend and tune the ABMSOD algorithm, originally used to detect 2D objects in non-medical images, to localize 3D anatomical structures in medical volumes. For fast localization, we design and develop optimized parallel implementations of the proposed algorithm on multi-cores using OpenMP, and on GPUs using CUDA. We evaluate the quality, performance and scalability of the proposed algorithm on Computed Tomography (CT) volumes for various structures.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Effective and fast localization of anatomical structures is a crucial first step towards automated analysis of medical volumes. In this paper, we propose an iterative approach for structure localization in medical volumes based on the adaptive bandwidth mean-shift algorithm for object detection (ABMSOD). We extend and tune the ABMSOD algorithm, originally used to detect 2D [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[89,38,3],"tags":[479,478,14,1788,20,378,567],"class_list":["post-8504","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-medicine","category-paper","tag-computed-tomography","tag-ct","tag-cuda","tag-medicine","tag-nvidia","tag-tesla-c2050","tag-tomography"],"views":2587,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8504","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=8504"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8504\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8504"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8504"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8504"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}