Automated Measurement of Vascular Calcification in Femoral
  Endarterectomy Patients Using Deep Learning
Alireza Bagheri Rajeoni Breanna Pederson Daniel G. Clair Susan M. Lessner Homayoun Valafar
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.