Elliptic Shape Prior Dynamic Programming for Accurate Vessel Segmentation in MRI Sequences with Automated Optimal Parameter Selection

Abstract

This study proposes an accurate vessel segmentation method using an elliptic-model-guided dynamic programming algorithm to find the shortest path in a two-dimensional matrix. The elliptic model increases the algorithm’s resistance to noise around the vessel boundaries. To test the system reliability and accuracy, we use phantom images with added uniformly distributed noise to get different signal-to-noise ratios. In addition, the optimal parameter values are determined via a phantom study. We further apply this method to detect the boundaries of the common carotid artery (CCA) in real magnetic resonance imaging (MRI) sequences without contrast agent injection. Manual tracings of the CCA boundaries are performed by well-trained experts as the gold standard. Comparisons between the manual tracings and automated results are made on 8 MRI sequences (400 total images). The average unsigned error rate is 3.6 % (standard deviation = 2.4 %). The results demonstrate that the proposed method is qualitatively better than traditional dynamic programming for vessel boundary detection on MRI sequences without contrast agent injection.