Coronary angiography image segmentation based on PSPNet
Introduction
Coronary artery disease (CAD) has a high incidence and high fatality rate, and is one of the most important causes of death worldwide [1], [2], [3]. The pathophysiological basis of CAD is atherosclerotic lesions in coronary arteries, which will cause vascular stenosis or blockage. The primary way of early diagnosis of coronary heart disease is the detection and quantification of coronary artery stenosis.
With the constant understanding of diseases and advancement of imaging technology, these have led to the continuous emergence of new diagnostic methods. Coronary angiography (CAG) is regarded as the gold standard in clinical treatment [4]. This is a non-invasive method of examining coronary arteries. It does not require an arterial catheter, but allows the contrast fluid to flow through the heart by intravenous injection, and complete the heart scans [5]. X-ray angiography has a powerful fluoroscopic ability to examine the structure of coronary arteries, and is the most commonly used technology from in the clinical diagnosis of CAD. This diagnosis and treatment method has opened another door for people to understand CAD. It can clearly and intuitively display the detailed images of the coronary arteries, allowing doctors and patients to observe that the coronary arteries have vascular wall calcification and stenosis problems, which make the diagnosis of coronary heart disease more intuitive and scientific.
CAG image blood vessel segmentation is of great significance in the assessing of the degree of vascular disease, assisting doctors in diagnosis and treatment, and reconstruction of the three-dimensional structure of blood vessels [6], [7], [8]. A good segmentation result can save the doctor's manual segmentation time and help the doctor correctly diagnose the condition. The segmented blood vessels can be used for further research, such as blood flow velocity, degree of vascular stenosis, etc., to lay the foundation for subsequent vascular dynamics research and three-dimensional reconstruction.
Convolutional neural network (CNN), relying on weight sharing, automatic feature extraction and computer performance improvement, has achieved remarkable results [9]. The medical community has also noticed the great success of deep learning methods and hopes to apply these technologies to different tasks in medical image processing.
The use of deep learning technology to segment coronary vessels has three advantages. The first point is that it can automatically extract features [10]. In the past, meaningful or task-related parts were mainly identified by human experts. Based on their understanding of these images, these features can be carried out by non-experts in the field, using machine learning techniques. Now, deep learning has incorporated feature engineering steps into the scope of learning steps. In other words, there is no need to manually extract features. If necessary, deep learning only needs a small amount of pre-processed data, and then new features can be discovered in a self-learning manner.
The second point is that deep learning technology has good versatility [11]. Because of the difference in imaging environment and the level of radiologists, even the same patient will have different CAG images [12]. The traditional method uses the features designed by experts, who may have a good effect on a certain kind of image, but it is not universal. Deep learning technology uses a large number of high-quality labeled samples for training. This method can be applied to various types of contrast images.
The third point is that deep learning can improve efficiency and detection accuracy [13]. Coronary vascular images will inevitably introduce various noises during the imaging process, and the contrast agent will be unevenly distributed in the blood vessels due to various reasons. The vascular bifurcation will cause the traditional methods to be slow in the segmentation process. The accuracy is low and a lot of labor time is wasted. Deep learning technology relies on powerful feature extraction and classification. This enables deep learning technology to be more efficient and detection accuracy than traditional methods.
Section snippets
Target segmentation based on traditional algorithms
Currently, traditional image segmentation comprises threshold-based segmentation, region-based segmentation, edge-based segmentation, and specific theories-based segmentation. With the progress of research, segmentation algorithms are divided into pixel-based classification, threshold segmentation, edge detection, color image segmentation, depth image segmentation, and fuzzy set-based methods.
Data source
We will perform segmentation tasks and experiments on coronary angiography images. At present, the available coronary angiography image data is collected. This study established a database derived from real patient cases. We collected CAG images of 109 patients from the Fuwai Central China Cardiovascular Hospital.
Data preprocessing
During subsequent training, the network can only accept grayscale images as input, so we convert the blood vessel RGB value to a grayscale value from 0 to N-1 according to the
Discussion
At present, deep learning has achieved good diagnostic accuracy comparable to clinical experts and medical doctors in the field of medical imaging. However, in general, there are still many challenges in applying deep learning to the field of medical image processing.
First, there is still a gap between the segmentation accuracy and the current best method. Although deep learning has proven its great success in other fields, it has just entered the field of coronary artery segmentation, and many
Conclusion
Based on the coronary vascular imaging, the artery is a thin tubular structure, which has relatively low contrast and artifacts, and is difficult to accurately segment and effectively annotate the scarcity of samples. A PSPNet-based multi-scale CNN model is proposed. This algorithm first down-sample the pre-processed image to obtain images of multiple scales, and sends them to the CNN. The features of different scales are selected and fused in the fully connected layer, and finally the features
Declaration of Competing Interest
No.
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