DEEP LEARNING BASED ARTERY VESSEL SEGMENTATION IN CORONARY ANGIOGRAPHY

Abstract

Segmentation method of major vessels on X-ray coronary angiography using fully convolutional networks based on
DNN architecture. The division of coronary courses in angiography pictures is a crucial device to assess blood
vessel sicknesses and pick legitimate coronary treatment. The right division of coronary conduits has become a
significant theme for the enlistment of disparate modalities, which allow physician’s rapid access to different
medical imaging information from Computed Tomography (CT) scans or Magnetic Resonance Imaging (MRI). An
accurate fully automatic algorithm based on Frangi Filter for Major vessel centerline extraction, caliber
estimation, and catheter detection is proposed. The Vesselness, geodesic paths, and a new multistage edge map are
combined to customize the DNN approach to the segmentation structures, utilizing a global optimization of the
DNN energy function.The precision and recall values are higher than the U-Net values