title: Vessel extraction in X-ray angiograms using deep learning.
abstract: Coronary artery disease (CAD) is the most common type of heart disease which is the leading cause of death all over the world. X-ray angiography is currently the gold standard imaging technique for CAD diagnosis. These images usually suffer from low quality and presence of noise. Therefore, vessel enhancement and vessel segmentation play important roles in CAD diagnosis. In this paper a deep learning approach using convolutional neural networks (CNN) is proposed for detecting vessel regions in angiography images. Initially, an input angiogram is preprocessed to enhance its contrast. Afterward, the image is evaluated using patches of pixels and the network determines the vessel and background regions. A set of 1,040,000 patches is used in order to train the deep CNN. Experimental results on angiography images of a dataset show that our proposed method has a superior performance in extraction of vessel regions.
authors.forename: E
authors.initials: E
authors.lastname: Nasr-Esfahani
authors.forename: S
authors.initials: S
authors.lastname: Samavi
authors.forename: N
authors.initials: N
authors.lastname: Karimi
authors.forename: S M R
authors.initials: SM
authors.lastname: Soroushmehr
authors.forename: K
authors.initials: K
authors.lastname: Ward
authors.forename: M H
authors.initials: MH
authors.lastname: Jafari
authors.forename: B
authors.initials: B
authors.lastname: Felfeliyan
authors.forename: B
authors.initials: B
authors.lastname: Nallamothu
authors.forename: K
authors.initials: K
authors.lastname: Najarian
mesh_terms: D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000175:diagnosis*; D003331:Coronary Vessels / Q000000981:diagnostic imaging; D006801:Humans; D007858:Learning; D016571:Neural Networks, Computer; D014057:Tomography, X-Ray Computed; D014965:X-Rays