Coronary Centerline Extraction via Optimal Flow Paths and CNN Path Pruning
Mehmet Akif Gulsun, Gareth Funka-Lea, Puneet Kumar Sharma, Saikiran Rapaka, Yefeng Zheng · Lecture notes in computer science · 2016
We present a novel method for the automated extraction of blood vessel centerlines. There are two major contributions. First, in order to avoid the shortcuts to which minimal path methods are prone, we find optimal paths in a computed flow field. We solve for a steady state porous media flow inside a region of interest and trace centerlines as maximum flow paths. We explain how to estimate anisotropic orientation tensors which are used as permeability tensors in our flow field computation. Second, we introduce a convolutional neural network (CNN) classifier for removing extraneous paths in the detected centerlines. We apply our method to the extraction of coronary artery centerlines found in Computed Tomography Angiography (CTA). The robustness and stability of our method are enhanced by using a model-based detection of coronary specific territories and main branches to constrain the search space [ 15 ]. Validation against 20 comprehensively annotated datasets had a sensitivity and specificity at or above 90 %. Validation against 106 clinically annotated coronary arteries showed a sensitivity above 97 %. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.