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Real-time Lesion Detection of Cardiac Coronary Artery Using Deep Neural Networks

In the field of cardiac arterial interventional therapy, coronary angiography imaging provides key information to physicians for treatment strategy selection, while the lesion identification process is time-consuming and error-prone even for experienced doctors. This paper proposes a method for the automatic detection of lesion in cardiac coronary angiography based on the deep learning and convolution neural network for the very first time. We used 2925 medical images for building the model. Several lesions exist on the vessel of each image. We will regard these lesion areas as objects that are different from other background areas. We designed a model based on the convolution neural network, applying some advanced building block including CReLU, Inception and other advanced technology such as batch normalization, residual connections, skip-layer connection in our network model. After training, the network model can distinguish the difference between a lesion area and a normal vessel area (background), which can detect the location of the coronary artery lesion in real time without any manual intervention. For the stenosis lesion, the recall rate of detection achieves 0.88.