[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124782-en":3,"doc-seo-124782-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124782,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Automated Measurement of Vascular Calcification in Femoral Endarterectomy Patients Using Deep Learning","Atherosclerosis is a chronic inflammatory disease that increases global health risk, and precise interpretation of diagnostic CT angiography images is crucial for staging and tracking atherosclerosis-associated peripheral arterial disease (PAD). Manual CTA analysis is slow and burdensome, especially across hundreds of slices. This work applies deep learning to segment the arterial tree in PAD patients undergoing femoral endarterectomy and quantify vascular calcification from the left renal artery to the patella. Using CTA data from 27 patients, the model achieves 83.4% Dice accuracy for aorta-to-patella segmentation and provides calcification scores with MAPE of 9.5% and correlation 0.978 versus manual measurements.","Automated Measurement of Vascular Calcification in Femoral Endarterectomy Patients Using Deep Learning  \nAlireza Bagheri Rajeoni 1, Breanna Pederson 2, Daniel G. Clair 3, Susan M. Lessner 2,* and Homayoun Valafar 1,*  \n1 Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA; [alirezab@email.sc.edu](alirezab@email.sc.edu)  \n2 Department of Cell Biology and Anatomy, University of South Carolina School of Medicine, Columbia, SC 29209, USA; [pedersob@email.sc.edu](pedersob@email.sc.edu)  \n3 Department of Vascular Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA; [dan.clair@vumc.org](dan.clair@vumc.org)  \n* Correspondence: [susan.lessner@uscmed.sc.edu](susan.lessner@uscmed.sc.edu) (S.M.L.); [homayoun@cse.sc.edu](homayoun@cse.sc.edu) (H.V.)  \nAbstract: Atherosclerosis, a chronic inflammatory disease affecting the large arteries, presents a global health risk. Accurate analysis of diagnostic images, like computed tomographic angiograms (CTAs), is essential for staging and monitoring the progression of atherosclerosis-related conditions, including peripheral arterial disease (PAD). However, manual analysis of CTA images is time-consuming and tedious. To address this limitation, we employed a deep learning model to segment the vascular system in CTA images of PAD patients undergoing femoral endarterectomy surgery and to measure vascular calcification from the left renal artery to the patella. Utilizing proprietary CTA images of 27 patients undergoing femoral endarterectomy surgery provided by Prisma Health Midlands, we developed a Deep Neural Network (DNN) model to first segment the arterial system, starting from the descending aorta to the patella, and second, to provide a metric of arterial calcification. Our designed DNN achieved 83.4% average Dice accuracy in segmenting arteries from aorta to patella, advancing the state-of-the-art by 0.8%. Furthermore, our work is the first to present a robust statistical analysis of automated calcification measurement in the lower extremities using deep learning, attaining a Mean Absolute Percentage Error (MAPE) of 9.5% and a correlation coefficient of 0.978 between automated and manual calcification scores. These findings underscore the potential of deep learning techniques as a rapid and accurate tool for medical professionals to assess calcification in the abdominal aorta and its branches above the patella. The developed DNN model and related documentation in this project are available at GitHub page at [https://github.com/pip-alireza/DeepCalcScoring](https://github.com/pip-alireza/DeepCalcScoring).  \nKeywords: vasculature segmentation; deep learning; image segmentation; peripheral arterial disease; computed tomography angiogram; vascular calcification; ectopic calcification  \n1. Introduction  \nPeripheral Arterial Disease (PAD) is a chronic vascular condition that disrupts blood flow to the lower extremities. One of the distinctive features of PAD is arterial calcification, which can occur in association with atherosclerotic plaques or independently. As vascular calcification is emerging as an important indicator of cardiovascular health, it is imperative to conduct a comprehensive analysis for PAD staging and monitoring. However, the manual assessment of this process is excessively time-consuming and laborious, as the vascular system extends through hundreds of images in CTA scans.  \nWhile there have been efforts to leverage deep learning techniques for the automated extraction of the vascular system and calcium scoring, these endeavors have primarily focused on limited segments of the vascular network. To our knowledge, there is an absence of research aimed at extracting the vascular system spanning from the thoracic aorta down to the patella and automatically measuring calcium scores within this extracted network. In this study, we present a deep learning approach that automatically and accurately extractsand analyzes th","cbCaio72sycdbJu8","https://ap.wps.com/l/cbCaio72sycdbJu8","pdf",1527654,1,17,"English","en",105,"# Introduction\n## Peripheral arterial disease (PAD)\n## Vascular calcification and clinical relevance","[{\"question\":\"What problem does the study address in PAD imaging?\",\"answer\":\"Manual analysis of CTA images for vascular calcification is time-consuming and labor-intensive, hindering efficient staging and monitoring in PAD patients.\"},{\"question\":\"How does the deep learning model operate in this work?\",\"answer\":\"It uses a deep neural network to segment the arterial system from the descending aorta to the patella and then produces a quantitative metric of arterial calcification.\"},{\"question\":\"How accurate are the automated calcification measurements compared with manual scoring?\",\"answer\":\"Automated scores show a mean absolute percentage error (MAPE) of 9.5% and a correlation coefficient of 0.978 with manual calcification scores.\"}]","Automated Measurement of Vascular Calcification in Femoral Endarterectomy Patients Using Deep Learning | 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problem does the study address in PAD imaging?","Question",{"text":75,"@type":76},"Manual analysis of CTA images for vascular calcification is time-consuming and labor-intensive, hindering efficient staging and monitoring in PAD patients.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the deep learning model operate in this work?",{"text":80,"@type":76},"It uses a deep neural network to segment the arterial system from the descending aorta to the patella and then produces a quantitative metric of arterial calcification.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate are the automated calcification measurements compared with manual scoring?",{"text":84,"@type":76},"Automated scores show a mean absolute percentage error (MAPE) of 9.5% and a correlation coefficient of 0.978 with manual calcification 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