{"id":768,"date":"2025-07-20T17:42:01","date_gmt":"2025-07-20T17:42:01","guid":{"rendered":"https:\/\/ccds.ai\/?p=768"},"modified":"2025-08-10T18:15:36","modified_gmt":"2025-08-10T18:15:36","slug":"pelvic-bone-segmentation-guided-fracture-classification","status":"publish","type":"post","link":"https:\/\/ccds.ai\/?p=768","title":{"rendered":"Pelvic Bone Segmentation Guided Fracture Classification"},"content":{"rendered":"<div id='av_section_1'  class='avia-section av-av_section-4626b8e4cec458b6915ec5d17cf7764f main_color avia-section-default avia-no-border-styling  avia-builder-el-0  avia-builder-el-no-sibling  avia-bg-style-scroll container_wrap fullsize'  ><div class='container av-section-cont-open' ><main  role=\"main\" itemprop=\"mainContentOfPage\"  class='template-page content  av-content-full alpha units'><div class='post-entry post-entry-type-page post-entry-768'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-mdbypz0q-f999a88820e20588db7bae1d75276f54\">\n.avia-image-container.av-mdbypz0q-f999a88820e20588db7bae1d75276f54 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-mdbypz0q-f999a88820e20588db7bae1d75276f54 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-mdbypz0q-f999a88820e20588db7bae1d75276f54 av-styling- avia-align-center  avia-builder-el-1  el_before_av_textblock  avia-builder-el-first '   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><img fetchpriority=\"high\" fetchpriority=\"high\" decoding=\"async\" class='wp-image-769 avia-img-lazy-loading-not-769 avia_image ' src=\"https:\/\/ccds.ai\/wp-content\/uploads\/2025\/07\/Screenshot-2025-07-20-233931-e1753033749151.png\" alt='' title='Screenshot 2025-07-20 233931'  height=\"470\" width=\"1024\"  itemprop=\"thumbnailUrl\" srcset=\"https:\/\/ccds.ai\/wp-content\/uploads\/2025\/07\/Screenshot-2025-07-20-233931-e1753033749151.png 1024w, https:\/\/ccds.ai\/wp-content\/uploads\/2025\/07\/Screenshot-2025-07-20-233931-e1753033749151-300x138.png 300w, https:\/\/ccds.ai\/wp-content\/uploads\/2025\/07\/Screenshot-2025-07-20-233931-e1753033749151-768x353.png 768w, https:\/\/ccds.ai\/wp-content\/uploads\/2025\/07\/Screenshot-2025-07-20-233931-e1753033749151-705x324.png 705w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/div><\/div><\/div>\n<section  class='av_textblock_section av-mdbymg4q-830354290faeee6aff7f188220726ed2'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p><span style=\"font-weight: 400;\">Pelvic fractures are critical injuries that require timely and precise diagnosis. We are developing an AI-based automated computer-aided diagnosis (CAD) system to enhance the accuracy and reliability of pelvic fracture detection, ultimately supporting faster and more effective medical assessments.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The first step in our approach is multi-bone segmentation, a deep learning process that identifies and isolates the nine pelvic bones from the X-ray. The segmented bone masks are then aggregated to create a refined mask of the pelvic region.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Next, we extract the segmented X-ray from the original X-ray using the aggregated mask. This helps the system focus only on relevant areas instead of the entire X-ray. We then feed it into a separate deep learning model for classification. This classification model determines whether a fracture is present.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">To improve interpretability, we use GradCAM visualization, which highlights the critical areas the AI focuses during detection. This ensures the model&#8217;s decisions are transparent and aligned with anatomical relevance.<\/span><\/p>\n<p><b>Relevant publications:<\/b><\/p>\n<p>S. A. Ul Alam, S. Binte Alam, S. Saha, M. Haque, R. Rahman and S. Kobashi, &#8220;Pelvic bone region segmentation (PBRS) from X-ray image using convolutional neural network (CNN),&#8221; 2023 26th International Conference on Computer and Information Technology (ICCIT), Cox&#8217;s Bazar, Bangladesh, 2023, pp. 1-6, <strong>doi: 10.1109\/ICCIT60459.2023.10441155<\/strong>.<\/p>\n<\/div><\/section>\n\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":769,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[93],"tags":[],"class_list":["post-768","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-mira_projects"],"acf":[],"jetpack_featured_media_url":"https:\/\/ccds.ai\/wp-content\/uploads\/2025\/07\/Screenshot-2025-07-20-233931-e1753033749151.png","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/ccds.ai\/index.php?rest_route=\/wp\/v2\/posts\/768","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ccds.ai\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ccds.ai\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ccds.ai\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/ccds.ai\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=768"}],"version-history":[{"count":2,"href":"https:\/\/ccds.ai\/index.php?rest_route=\/wp\/v2\/posts\/768\/revisions"}],"predecessor-version":[{"id":772,"href":"https:\/\/ccds.ai\/index.php?rest_route=\/wp\/v2\/posts\/768\/revisions\/772"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ccds.ai\/index.php?rest_route=\/wp\/v2\/media\/769"}],"wp:attachment":[{"href":"https:\/\/ccds.ai\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=768"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ccds.ai\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=768"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ccds.ai\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=768"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}