Heart coronary arteries detection by deep learning methods

Coronary artery disease is the most dangerous heart disease caused by coronary artery disease. In clinical practice, X-ray coronary angiography is the main imaging method used to diagnose coronary artery disease. High cost and complexity of analysis of a large amount of data by a cardiac surgeon made it necessary to automate the process of image processing and diagnosis of stenoses. In this paper we considered models of deep detection, localisation and characterisation of stenoses using popular models SSD, R-FCN, Faster-­RCNN, RetinaNet, EfficientDet. The models were pre-trained on the COCO image set and varied on the underlying neural network architecture. Computational experiments on stenosis detection from X-ray images were performed on the coronary angiography data used. The data consist of 9378 clinically acquired video sequences from invasive coronary angiography performed in DICOM format and labelled into individual frames for each video containing coronary artery stenosis. A total of 1593 image sequences with a resolution of (512×512) pixels were annotated. A comparative analysis of the models in terms of the main performance indicators: mAP accuracy, image processing time, number of model parameters was carried out. The obtained results allow us to state that the Faster R-CNN (ResNet101) and EfficientDet D4 (ResNet101) models are the detectors of choice in the detection of coronary artery stenosis. They have high detection accuracy and image processing speed compared to other models, as well as relatively low weights of parametrics. Comparative analysis of their performance with the results of other researchers showed superior or comparable results obtained in this work.