TransONet: Automatic Segmentation of Vasculature in Computed Tomographic
  Angiograms Using Deep Learning
Alireza Bagheri Rajeoni Breanna Pederson Ali Firooz Hamed Abdollahi Andrew K. Smith Daniel G. Clair Susan M. Lessner Homayoun Valafar
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 https://github.com/pip-alireza/TransOnet.