Abstract
The automatic segmentation of main vessels on X-ray angiography (XRA) images is of great importance in the smart coronary artery disease diagnosis system. However, existing methods have been developed to this task, but these methods have difficulty in recognizing the coronary artery structure in XRA images. Main vessel segmentation is still a challenging task due to the diversity and small-size region of the vessel in the XRA images. In this study, we propose a robust method for main vessel segmentation by using deep learning architectures with fully convolutional networks. Four deep learning models based on the UNet architecture are evaluated on a clinical dataset, which consists of 3200 X-ray angiography images collected from 1118 patients. Using the precision (Pre), recall (Re), and F1 score (F1) as evaluation metrics, the average Pre, Re, and F1 for main vessel segmentation in the entire experimental dataset is 0.901, 0.898, and 0.900, respectively. 89.8% of the images exhibited a high F1 score >0.8. For the main vessel segmentation in XRA images, our deep learning methods demonstrated that vessels could be segmented in real time with a more optimized implementation, to further facilitate the online diagnosis in smart medical.
Highlights
Automatic medical diagnosis is of importance in the construction of smart medical. e smart medical can provide data collection, data analysis, and online diagnosis for the patients in anywhere
This subjective visual diagnosis inevitably causes unreliable assessment of the lesions, which decreases the operative workflow efficiency and raises the health hazards to patients. us, in the X-ray angiography (XRA) analysis system, the automated coronary artery segmentation on XRA images is important for the diagnosis and intervention of cardiovascular diseases
To assess the segmentation performance of the deep learning methods based on UNet architecture, we evaluated the precision, recall, and F1 score
Summary
Automatic medical diagnosis is of importance in the construction of smart medical. e smart medical can provide data collection, data analysis, and online diagnosis for the patients in anywhere. Erefore, the automatic analysis of coronary imaging data is necessary in smart medical. X-ray angiography (XRA) is the main imaging methodology to guide the coronary artery disease diagnosis. With the help of the contrast-enhanced XRA images, doctors can diagnose the coronary artery disease and evaluate therapeutic effects based on the morphological characteristics. This subjective visual diagnosis inevitably causes unreliable assessment of the lesions, which decreases the operative workflow efficiency and raises the health hazards to patients. Us, in the XRA analysis system, the automated coronary artery segmentation on XRA images is important for the diagnosis and intervention of cardiovascular diseases This subjective visual diagnosis inevitably causes unreliable assessment of the lesions, which decreases the operative workflow efficiency and raises the health hazards to patients. us, in the XRA analysis system, the automated coronary artery segmentation on XRA images is important for the diagnosis and intervention of cardiovascular diseases
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