Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

Abstract

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.

Deep Learning for Cardiac Image Segmentation: A Review

“…In addition, the work of Zreik et al (2018a) has proposed a method for the automatic identification of patients with significant coronary artery stenoses through the segmentation and analysis of the LV myocardium. In this work, a multi-scale FCN is first employed for myocardium segmentation, and then a convolutional autoencoder is used to characterize the LV myocardium, followed by a support vector machine (SVM) to classify patients based on the extracted features.…”

Deep Learning in Cardiology

“…Method Application/Notes a CT Lessman 2016 [195] CNN detect coronary calcium using three independently trained CNNs Shadmi 2018 [196] DenseNet compared DenseNet and u-net for detecting coronary calcium Cano 2018 [197] CNN 3D regression CNN for calculation of the Agatston score Wolterink 2016 [198] CNN detect coronary calcium using three CNNs for localization and two CNNs for detection Santini 2017 [199] CNN coronary calcium detection using a seven layer CNN on image patches Lopez 2017 [200] CNN thrombus volume characterization using a 2D CNN and postprocessing Hong 2016 [201] DBN detection, segmentation, classification of abdominal aortic aneurysm using DBN and image patches Liu 2017 [202] CNN left atrium segmentation using a twelve layer CNN and active shape model (STA13) de Vos 2016 [203] CNN 3D localization of anatomical structures using three CNNs, one for each orthogonal plane Moradi 2016 [204] CNN detection of position for a given CT slice using a pretrained VGGnet, handcrafted features and SVM Zheng 2015 [205] Multiple carotid artery bifurcation detection using multi-layer perceptrons and probabilistic boosting-tree Montoya 2018 [206] ResNet 3D reconstruction of cerebral angiogram using a 30 layer ResNet Zreik 2018 [207] CNN, AE identify coronary artery stenosis using CNN for LV segmentation and an AE, SVM for classification Commandeur 2018 [208] CNN quantification of epicardial and thoracic adipose tissue from non-contrast CT Gulsun 2016 [209] CNN extract coronary centerline using optimal path from computed flow field and a CNN for refinement CNN carotid intima media thickness video interpretation using two CNNs with two layers on Ultrasound Tom 2017 [226] GAN IVUS image generation using two GANs (IV11) Wang 2017 [227] CNN breast arterial calcification using a ten layer CNN on mammograms Liu 2017 [228] CNN CAC detection using CNNs on 1768 X-Rays Pavoni 2017 [229] CNN denoising of percutaneous transluminal coronary angioplasty images using four layer CNN Nirschl 2018 [230] CNN trained a patch-based six layer CNN for identifying heart failure in endomyocardial biopsy images Betancur 2018 [231] CNN trained a three layer CNN for obstructive CAD prediction from myocardial perfusion imaging a Results from these imaging modalities are not reported in this review because they were highly variable in terms of the research question they were trying to solve and highly inconsistent in respect with the use of metrics. Additionally all papers use private databases besides…”

“…CT has also been used for other tasks. Zreik et al [207] created a method to identify patients with coronary artery stenoses from the LV myocardium in rest CT. They used a multi-scale CNN to segment the LV myocardium and then encoded it using an unsupervised convolutional AE.…”

Prospects of deep learning for medical imaging

“…CT Heart Disease detection [40] PET/CT Bone Lesion detection [41] Retina image Retina Lesion detection [17] Endoscopic image Stomach Disease detection [42] Histology image Breast Lesion detection [43] Prediction [54] classified skin cancer photographs into binary classes and even suggested that such operation could be performed using portable devices such as smartphones. In histology, one study showed that cell differentiation could be classified with CNN, and the results have the potential to identify tissue or organ regeneration [55].…”