A graph-based approach for spatio-temporal segmentation of coronary arteries in X-ray angiographic sequences. (1st December 2016)

• Record Type: Journal Article
• Authors: M'hiri, Faten; Duong, Luc; Desrosiers, Christian; Leye, Mohamed; Miró, Joaquim; Cheriet, Mohamed
• Is Part Of: Computers in biology and medicine. Volume 79(2016)
• Page Start: 45
• Page End: 58
• Publication Date: 2016-12-01
• Subjects: Segmentation -- Graph-based method -- Tracking -- Coronary arteries -- Random walker -- X-ray angiography -- Superpixels; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2016.10.001
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The segmentation and tracking of coronary arteries (CAs) are critical steps for the computation of biophysical measurements in pediatric interventional cardiology. In the literature, most methods are focused on either segmenting the vessel lumen or on tracking the vessel centerline. However, they do not simultaneously combine the segmentation and tracking of a specific CA. This paper introduces a novel algorithm for CA segmentation and tracking from 2D X-ray angiography sequences. The proposed algorithm is based on the Temporal Vessel Walker (TVW) segmentation method, which combines graph-based formulation and temporal priors. Moreover, superpixel groups are used by TVW as image primitives to ensure a better extraction of the CA. The proposed algorithm, TVW with superpixels (SP-TVW), returns an accurate result to segment and track the artery along the angiogram. Quantitative results over 12 sequences of young patients show the accuracy of the proposed framework. The results return a mean recall of 84% in the dataset. In addition, the proposed method returned a Dice index of 70% in segmenting and tracking right coronary arteries and circumflex arteries. The performance of the proposed method surpasses the existing polyline method in tracking the centerline of CA with a more precise localization of the centerline, resulting in a smaller distance error of 0.23 mm compared to 0.94 mm. Abstract : Highlights: The model is defined for the segmentation and tracking of arteries in X-ray sequences. The model tracks a specific section of an artery instead of the entire coronary tree. The model combines a graph formulation, vesselness features and superpixels. The method enables for accurate tracking and segmentation of coronary arteries.