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, preprocessing 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. INDEX TERMS Angiogram, convolutional neural networks, deep learning, left anterior descending artery, selective feature map, selective filter, vessel segmentation.

Deep learning segmentation of major vessels in X-ray coronary angiography

“…10 ), LAD (0.676 in Jo et al . 11 ), and three major vessels including normal arteries (0.890 in Jun et al . 12 ).…”

“…First, to compare the segmentation performance of deep learning networks, cross validation was applied to each fold comprising three major vessels (Table 2). Then, to investigate the impact of the dataset composition, deep learning analyses were conducted with a separate dataset for each single major vessel, similar to the previous approaches 10,11 . In the cross validation, the fold proportion of training, validation, and test sets was 3:1:1, and the fold composition was changed in sequence under cyclic permutation.…”

“…Recently, deep learning models have been introduced for CAG segmentation 8–12 . However, deep learning approaches for major vessel segmentation have not achieved prediction accuracy sufficient for clinical applications 10,11 .…”

A New Method for Segmentation of the Coronary Arteries of Interest and Diameter Measurement

“…Yang et al [24] added an image mask channel to the CNN single-channel, and the dual-channel processing was more effective in removing catheter regions and artifacts. Jo et al [25] proposed a selective feature mapping method for segmenting the left front descending trunk in cardiovascular images. Fan et al [26] used both the image mask information before angiography and the blood vessel information after angiography as multichannel input to enhance the structure information of blood vessels, and also matched the mask image with the blood vessel image to obtain a more accurate aligning effect.…”

Real-Time Coronary Artery Stenosis Detection Based on Modern Neural Networks

“…The key stage that determines the speed and accuracy of such algorithms is based on the coronary artery tree extraction using the centerline extraction 7,8 ; the graph-based method [9][10][11] ; superpixel mapping 12,13 ; and machine/deep learning [14][15][16] .…”