Coronary atherosclerotic heart disease is one of the main causes of death from cardiovascular diseases. Early detection of atherosclerotic lesions can help clinicians understand the condition of cardiovascular patients and provide reference for better treatment measures. Compared with other detection technologies, intravascular optical coherence tomography (IVOCT) has the advantages of no radiation, high resolution, and high imaging speed. Therefore, it plays an important role in the detection and evaluation of atherosclerotic plaque. Although IVOCT has been widely used in the detection of plaque in coronary vessels, the imaging system could not directly provide effective plaque feature identification information, and clinicians can only judge the characteristics of plaque according to their own experience. Based on a brief introduction of the application of IVOCT in detecting coronary atherosclerotic plaque, this paper introduces the method of eliminating the vascular motion artifact caused by cardiac pulsation. The automatic segmentation and classification of IVOCT images are studied by using machine learning method. And the plaque features of calcified plaque, lipid plaque and fibrous plaque in IVOCT images are extracted. The deep learning algorithm is used to analyze the characteristics of vulnerable plaque and put forward quantitative evaluation indicators. It is very important to develop the intelligent recognition system of IVOCT in plaque type, that provide objective, intuitive and accurate plaque classification marks, display and rupture risk assessment for the clinic. So that clinicians can get rid of the current situation of relying solely on experience for lesion recognition, and save patients' lives in time.
KEYWORDS: Manufacturing, Visualization, Visual analytics, Data modeling, 3D modeling, Telecommunications, Software, Computer simulations, 3D visualizations, Visual process modeling
In order to meet the monitoring requirements of the upper management of the enterprise for the production status of the production line, research on the real-time status visualization of the intelligent manufacturing production line based on digital twin is carried out. First, the visualization system architecture of intelligent manufacturing production line based on digital twin is built, and its key realization process is clarified; then, it focuses on three key technologies: real-time collection method of production data based on CNC (Computer numerical control) system and RFID technology, data management based on MySQL, information visualization and push, which elaborates the visualization method in detail. Finally, the automation production line of an intelligent manufacturing laboratory in a university is taken as an application case to realize its visualization.
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