Research Information System
30th Signal Processing and Communications Applications Conference, SIU 2022, Safranbolu, Turkey, 15 - 18 May 2022, (Full Text)
© 2022 IEEE.Cardiovascular diseases are the most common diseases in our age and are known to be the cause of one-third of the mortality rates in the world unless they are treated. The core priority for the treatment of these diseases is to make the correct diagnosis. Although there are lots of methods such as computer tomography angiography (CTA) and magnetic resonance angiography (MRA) in the diagnosis of cardiovascular diseases, the most used method for diagnosis is the X-RAY angiography. X-RAY angiography images provide 2D vein images and the next step for a complete diagnosis is that surgeons/doctors interpret these images through their experiences, make a diagnosis and determine the treatment method. In this paper, it has been studied to reconstruct 3D synthetic coronary artery vessels tree by using synthetic segmented 2D x-ray angiography images and the pose values of these images with a view to providing early treatment of cardiovascular diseases as a result of early diagnosis and facilitating the life of doctors. In order to obtain a 3D coronary vessel, the fully connected convolutional neural networks model in which we have input the synthetically prepared segmented 2D x-ray vessel images and their pose values and whereby obtained a 3D vessel image has been reconstructed. This model has been introduced for the first time in the literature in this research. The synthetic 3D vessels tree has been successfully reconstructed as a result of the tests performed with the segmented synthetic data. As a result of this work, it is aimed to decrease the mortality rates related to wrong or late diagnosis in cardiovascular diseases.