Three-Dimensional Reconstruction of Vascular Model in Intravascular Ultrasound Images Using Semantic Segmentation.

Coronary atherosclerotic disease is a major cause of myocardial infarction and usually causes partial or complete coronary artery disorders, which can be life-threatening in severe cases. Currently, the diagnosis of coronary atherosclerosis is mainly achieved through intravascular ultrasound (IVUS). By using intravascular ultrasound, the location and morphology of lesions can be identified early, which is crucial for the early detection and accurate diagnosis of coronary artery disease. Intravascular ultrasound is a widely used imaging technique for diagnosing and treating cardiovascular diseases. In this paper, we propose a new method for three-dimensional (3D) reconstruction of IVUS vascular models based on semantic segmentation. The proposed method utilizes state-of-the-art deep learning techniques to accurately segment the blood vessels in IVUS images. The proposed method utilizes state-of-the-art deep learning techniques to accurately segment the blood vessels in IVUS images. Subsequently, the segmented vessels are used to generate 3D reconstructions of the vascular models. Our method achieves high accuracy and robustness, and it has the potential to enhance the accuracy of IVUS-based diagnosis and treatment planning. Experimental results on a dataset of real-world IVUS images demonstrate the effectiveness of our method.