Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography

Abstract

Visual identification of coronary arterial lesion from three-dimensional coronary computed tomography angiography (CTA) remains challenging. We aimed to develop a robust automated algorithm for computer detection of coronary artery lesions by machine learning techniques. A structured learning technique is proposed to detect all coronary arterial lesions with stenosis [Formula: see text]. Our algorithm consists of two stages: (1) two independent base decisions indicating the existence of lesions in each arterial segment and (b) the final decision made by combining the base decisions. One of the base decisions is the support vector machine (SVM) based learning algorithm, which divides each artery into small volume patches and integrates several quantitative geometric and shape features for arterial lesions in each small volume patch by SVM algorithm. The other base decision is the formula-based analytic method. The final decision in the first stage applies SVM-based decision fusion to combine the two base decisions in the second stage. The proposed algorithm was applied to 42 CTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis [Formula: see text]. Visual identification of lesions with stenosis [Formula: see text] by three expert readers, using consensus reading, was considered as a reference standard. Our method performed with high sensitivity (93%), specificity (95%), and accuracy (94%), with receiver operator characteristic area under the curve of 0.94. The proposed algorithm shows promising results in the automated detection of obstructive and nonobstructive lesions from CTA.

Keywords: coronary arterial disease; coronary arterial lesion detection from coronary computed tomography angiography; coronary computed tomography angiography; image feature extraction; learning-based detection; machine learning; structured learning; support vector machines; support vector regression.

Fig. 1

Flow chart of the learning-based algorithm as a base decision.

Fig. 2

Small volume patches as inputs for feature extraction and support vector machine (SVM) classification.

Fig. 3

An example of linearized volume with ground truth in blue box (expert readers’ marking) is shown (first row). Overlapping volume patches in lesion areas and nonoverlapping volume parches in normal areas (second row) are also shown.

Fig. 4

Flow chart of the analytic algorithm method.

Fig. 5

Example of lumen segmentation and lesion detection in a linearized volume in left anterior descending (LAD) artery. Range of the proximal LAD lesion (stenosis 25 to 49%) marked by expert is shown as a small box at around $x=27$ to 48 mm. Lumen diameters computed from the segmented lumen are shown and their cropped lumen diameters by anatomical knowledge are also shown. Expected normal luminal diameter is derived from the scan by automated piecewise line-fitting between branch points and takes into account normal tapering present in the dataset. The locations of the lesions with $\ge 25\%$ stenosis detected by the algorithm, concordant with the expert observer, are marked with vertical arrows.

Fig. 6

In the SVM-based learning algorithm as a first-level base decision, the improved sensitivity and balanced accuracy by data balancing scheme between normal class and lesion class are shown in (a) and (b).

Fig. 7

Performance variability according to the different small volume patch sizes at the first-level base decision, the learning-based algorithm.

Fig. 8

Decision fusion results with SVM classification with kernels of polynomial of (a) order 1, (b) order 2, (c) order 4, and (d) order 5 are shown. 252 coronary artery segments are displayed as points in the plot. The segments with lesions are shown in red and the normal segments are shown in blue in the SVM classification results. We chose the kernel function in order not to miss the true lesions (green circle).

Fig. 9

Receiver operator characteristics curve for the decision fusion algorithm.

Fig. 10

(a) An example of false positives by a previous work, but not detected by the proposed algorithm: expert readers graded it $<25\%$ stenosis and (b) detection of lesion with stenosis by both Ref.  and the proposed algorithm. Arrows indicate the location of lesions. Detected lesions with stenosis by mixed plaque in the proximal segment (70% stenosis by quantitative analysis and 90 to 99% stenosis by expert visual grading) are shown.