Segmentation of the Main Vessel of the Left Anterior Descending Artery Using Selective Feature Mapping in Coronary Angiography

Abstract

X-ray angiography, used in the evaluation of coronary artery disease, presents difficulties in the performance of quantitative coronary angiography analysis, by identifying major vessels. These difficulties are due to problems such as non-uniform illumination, low contrast ratio, and the presence of other tissues. Therefore, segmentation of the desired vessels in images containing multiple blood vessels is clinically important. This paper proposes selective feature mapping as a method for segmenting the left anterior descending artery main vessel in coronary angiography images. The proposed method consists of two steps for generating a candidate area of an image and then segmenting it. To generate the candidate area, feature maps that overlap significantly with the area of the ground truth are selected and combined. Segmentation then is performed using a neural network that learns only the ground truth region of the input image. The proposed method consists of eight modules: pre-processing of the angiogram, resizing of ground truth, pre-processing for segmentation, post-processing for segmentation, network, and segmentation network. This method has a precision of about 0.066, recall of 0.091, and an F1 score of 0.094, values which are higher than those generated by the U-Net, one of the conventional techniques.

Keywords

• Angiogram
• convolutional neural networks
• deep learning
• left anterior descending artery
• selective feature map
• selective filter