Plaque components segmentation in carotid artery on simultaneous non-contrast angiography and intraplaque hemorrhage imaging using machine learning

Abstract

PurposeThis study sought to determine the feasibility of using Simultaneous Non-contrast Angiography and intraPlaque Hemorrhage (SNAP) to detect the lipid-rich/necrotic core (LRNC), and develop a machine learning based algorithm to segment plaque components on SNAP images. MethodsSixty-eight patients (age: 58±9 years, 24 males) with carotid artery atherosclerotic plaque were imaged on a 3 T MR scanner with both traditional multi-contrast vessel wall MR sequences (TOF, T1W, and T2W) and 3D SNAP sequence. The manual segmentations of carotid plaque components including LRNC, intraplaque hemorrhage (IPH), calcification (CA) and fibrous tissue (FT) on traditional multi-contrast images were used as reference. By utilizing the intensity and morphological information from SNAP, a machine learning based two steps algorithm was developed to firstly identify LRNC (with or without IPH), CA and FT, and then segmented IPH from LRNC. Ten-fold cross-validation was used to evaluate the performance of proposed method. The overall pixel-wise accuracy, the slice-wise sensitivity & specificity & Youden's index, and the Pearson's correlation coefficient of the component area between the proposed method and the manual segmentation were reported. ResultsIn the first step, all tested classifiers (Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT) and Artificial Neural Network (ANN)) had overall pixel-wise accuracy higher than 0.88. For RF, GBDT and ANN classifiers, the correlation coefficients of areas were all higher than 0.82 (p < 0.001) for LRNC and 0.79 for CA (p < 0.001), and the Youden's indexes were all higher than 0.79 for LRNC and 0.76 for CA, which were better than that of NB and SVM. In the second step, the overall pixel-wise accuracy was higher than 0.78 for the five classifiers, and RF achieved the highest Youden's index (0.69) with the correlation coefficients as 0.63 (p < 0.001). ConclusionsThe RF is the overall best classifier for our proposed method, and the feasibility of using SNAP to identify plaque components, including LRNC, IPH, CA, and FT has been validated. The proposed segmentation method using a single SNAP sequence might be a promising tool for atherosclerotic plaque components assessment.