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.

翻译：人体血管系统的病理性改变是许多慢性疾病（如动脉粥样硬化和动脉瘤）的基础。然而，人工分析血管系统的诊断图像（例如计算机断层血管造影，CTA）既耗时又繁琐。为解决此问题，我们提出了一种深度学习模型，用于分割接受外周动脉疾病（PAD）手术患者的CTA图像中的血管系统。本研究聚焦于利用深度学习技术，在CTA图像中精确分割（1）从胸降主动脉至髂动脉分叉处以及（2）从胸降主动脉至膝部的血管系统。我们的方法在测试数据集上分别达到了93.5%和80.64%的平均Dice精度，凸显了其高准确性和潜在的临床实用性。这些结果表明，深度学习技术可作为医疗专业人员高效准确分析血管系统健康状况的宝贵工具。请访问本文GitHub页面：https://github.com/pip-alireza/TransOnet。