Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
Department of Radiology, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
Med Image Anal. 2018 Feb;44:72-85. doi: 10.1016/j.media.2017.11.008. Epub 2017 Nov 26.
In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The study includes consecutively acquired CCTA scans of 166 patients who underwent invasive FFR measurements. To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages. First, the LV myocardium is segmented using a multiscale convolutional neural network (CNN). To characterize the segmented LV myocardium, it is subsequently encoded using unsupervised convolutional autoencoder (CAE). As ischemic changes are expected to appear locally, the LV myocardium is divided into a number of spatially connected clusters, and statistics of the encodings are computed as features. Thereafter, patients are classified according to the presence of functionally significant stenosis using an SVM classifier based on the extracted features. Quantitative evaluation of LV myocardium segmentation in 20 images resulted in an average Dice coefficient of 0.91 and an average mean absolute distance between the segmented and reference LV boundaries of 0.7 mm. Twenty CCTA images were used to train the LV myocardium encoder. Classification of patients was evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation experiments and resulted in an area under the receiver operating characteristic curve of 0.74 ± 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the corresponding specificity was 0.77, 0.71 and 0.59, respectively. The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally significant coronary artery stenosis. This might reduce the number of patients undergoing unnecessary invasive FFR measurements.
在冠状动脉狭窄程度中等的患者中，需要确定其功能意义。在临床实践中，最常使用在有创冠状动脉造影（ICA）期间进行的血流储备分数（FFR）测量。为了减少 ICA 程序的数量，我们提出了一种使用深度学习分析静息冠状动脉 CT 血管造影（CCTA）中左心室（LV）心肌的方法，以识别具有功能性冠状动脉狭窄的患者。该研究包括连续采集了 166 例接受有创 FFR 测量的患者的 CCTA 扫描。为了识别具有功能性冠状动脉狭窄的患者，分析分几个阶段进行。首先，使用多尺度卷积神经网络（CNN）对 LV 心肌进行分割。为了对分割后的 LV 心肌进行特征描述，接下来使用无监督卷积自动编码器（CAE）对其进行编码。由于预计缺血变化会局部出现，因此将 LV 心肌分为多个空间连接的簇，并计算编码的统计信息作为特征。然后，使用基于提取特征的 SVM 分类器根据存在功能性狭窄来对患者进行分类。对 20 张 LV 心肌图像的分割进行定量评估，平均 Dice 系数为 0.91，分割和参考 LV 边界之间的平均绝对距离为 0.7mm。使用 20 张 CCTA 图像对 LV 心肌编码器进行训练。在 50 次 10 折交叉验证实验中，对剩余的 126 张 CCTA 扫描图像进行了患者分类评估，受试者工作特征曲线下面积为 0.74±0.02。在灵敏度水平为 0.60、0.70 和 0.80 时，相应的特异性分别为 0.77、0.71 和 0.59。结果表明，无需评估冠状动脉解剖结构，仅从静息状态下单次 CCTA 扫描中自动分析 LV 心肌，即可用于识别具有功能性冠状动脉狭窄的患者。这可能会减少不必要的有创 FFR 测量的患者数量。
