Automated Coronary Artery Atherosclerosis Detection and Weakly Supervised Localization on Coronary CT Angiography with a Deep 3-Dimensional Convolutional Neural Network
This work addresses the problem of screening for coronary artery disease in patients with acute chest pain, offering a potentially useful tool for physicians, though it is incremental as it applies existing deep learning methods to medical imaging.
The paper tackles automated detection of coronary artery atherosclerosis from coronary CT angiography using a deep 3D convolutional neural network, achieving an accuracy of 90.9% and a high negative predictive value of 96.1% on a dataset of 493 patients.
We propose a fully automated algorithm based on a deep learning framework enabling screening of a coronary computed tomography angiography (CCTA) examination for confident detection of the presence or absence of coronary artery atherosclerosis. The system starts with extracting the coronary arteries and their branches from CCTA datasets and representing them with multi-planar reformatted volumes; pre-processing and augmentation techniques are then applied to increase the robustness and generalization ability of the system. A 3-dimensional convolutional neural network (3D-CNN) is utilized to model pathological changes (e.g., atherosclerotic plaques) in coronary vessels. The system learns the discriminatory features between vessels with and without atherosclerosis. The discriminative features at the final convolutional layer are visualized with a saliency map approach to provide visual clues related to atherosclerosis likelihood and location. We have evaluated the system on a reference dataset representing247 patients with atherosclerosis and 246 patients free of atherosclerosis. With five-fold cross-validation,an Accuracy = 90.9%, Positive Predictive Value = 58.8%, Sensitivity = 68.9%, Specificity of 93.6%, and Negative Predictive Value (NPV) = 96.1% are achieved at the artery/branch level with threshold 0.5. The average area under the receiver operating characteristic curve is 0.91. The system indicates a high NPV, which may be potentially useful for assisting interpreting physicians in excluding coronary atherosclerosis in patients with acute chest pain.