Method for constructing three-dimensional heart model based on ultrasonic imaging
A technology of ultrasound imaging and three-dimensional models, applied in 3D modeling, neural learning methods, biological neural network models, etc., can solve the problems of consuming a lot of manpower and time, not being able to meet non-invasiveness, promotion restrictions, etc., and achieve high accuracy, Accurate heart volume and systolic function, the effect of great application value
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Embodiment 1
[0070] A method for constructing a three-dimensional heart model based on ultrasound imaging, comprising the steps of:
[0071] S1. Ultrasound image preprocessing: import the color Doppler cardiac ultrasound image data set, perform feature point recalibration processing on the data set, and then separate the data sets of each part of the heart (divided into left atrial data set, left ventricular data set, right Atrium dataset, right ventricle dataset, sinoatrial node dataset, atrioventricular node dataset, aortic dataset, pulmonary artery dataset, pulmonary vein dataset, superior and inferior vena cava dataset), ultrasonic speckle removal is performed for each dataset noise processing;
[0072] S2, image segmentation and edge contour feature extraction;
[0073] S3. Three-dimensional reconstruction of the heart model: the PTAM algorithm is used to carry out three-dimensional modeling of various parts of the heart, and then the generated single heart part models are synthesize...
Embodiment 2
[0100] A method for constructing a three-dimensional heart model based on ultrasound imaging, comprising the steps of:
[0101]S1. Ultrasound image preprocessing: import the color Doppler cardiac ultrasound image data set, perform feature point recalibration processing on the data set, and then separate the data sets of each part of the heart (divided into left atrial data set, left ventricular data set, right Atrium dataset, right ventricle dataset, sinoatrial node dataset, atrioventricular node dataset, aortic dataset, pulmonary artery dataset, pulmonary vein dataset, superior and inferior vena cava dataset), ultrasonic speckle removal is performed for each dataset noise processing;
[0102] S2. Image segmentation and edge contour feature extraction: The improved full convolutional neural network learning algorithm is used to segment and edge contour extraction of cardiac ultrasound images, and the preprocessed cardiac ultrasound image datasets are respectively input to the ...
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