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 this https URL

动脉粥样硬化是一种影响大动脉的慢性炎症性疾病；使用深度学习模型分割 CTA 图像中的血管系统以及计算血管钙化程度的 Metric，可提供医学专业人员对腹主动脉和膝下的钙化进行快速准确的评估。

使用深度学习自动测量股动脉内膜剥脱术患者的血管钙化

Automated Measurement of Vascular Calcification in Femoral  Endarterectomy Patients Using Deep Learning

Pathological alterations in the human vascular system underlie many chronic
diseases, such as atherosclerosis and aneurysms. However, manually analyzing
diagnostic images of the vascular system, such as computed tomographic
angiograms (CTAs) is a time-consuming and tedious process. To address this
issue, we propose a deep learning model to segment the vascular system in CTA
images of patients undergoing surgery for peripheral arterial disease (PAD).
Our study focused on accurately segmenting the vascular system (1) from the
descending thoracic aorta to the iliac bifurcation and (2) from the descending
thoracic aorta to the knees in CTA images using deep learning techniques. Our
approach achieved average Dice accuracies of 93.5% and 80.64% in test dataset
for (1) and (2), respectively, highlighting its high accuracy and potential
clinical utility. These findings demonstrate the use of deep learning
techniques as a valuable tool for medical professionals to analyze the health
of the vascular system efficiently and accurately. Please visit the GitHub page
for this paper at this https URL

通过提出基于深度学习模型的方法，可以高效且准确地分割 CTA 图像中与周围动脉疾病（PAD）手术相关的下行主动脉至髂分叉和下行主动脉至膝盖部分，为医学专业人员分析血管系统健康提供了有价值的工具。