Deep learning based optimization method for coronary arteriography image segmentation

Abstract

The invention discloses a deep learning based optimization method for coronary arteriography image segmentation. The method includes using a Tensor object for storing a coronary arteriography image and obtaining a segmentation result through accelerated calculation in a neural network through a GPU; and optimizing the segmentation result of the coronary arteriography image through a network structure formed by combination of a cascaded module and a pixel restoration module added in the neural network. According to the invention, the speed can be improved by 0.083s in a single image iteration process and more than 1 minute can be saved for data sets of thousands of levels in quantity in reality life. Besides, the neural network generally used for image styles is trained at least for 100 thousand times and more than 100 minutes can be saved in training of the whole network. At the same time, partial structure of the network is changed, so that the method ensures image segmentation accuracy and also has an advantage of reducing time length substantially and improves the segmentation accuracy.

Description

technical field [0001] The invention relates to an optimization method for segmenting coronary angiography images based on deep learning, and belongs to the field of optimization technology. Background technique [0002] Coronary angiography is a commonly used medical image for clinical diagnosis and treatment. It can help doctors distinguish stenosis, calcification and other information for diagnosis and treatment. Nowadays, there are technologies that use deep learning methods to segment images to process coronary angiograms. [0003] The existing technology is generally based on the deep learning method to segment blood vessels. The network structure is generally based on the cumulative deepening network structure of the convolutional layer. The image is extracted after each convolutional layer, but the convolution itself is a very time-consuming and memory-intensive process. The method of resources, and the way of image storage will affect the efficiency of the computer...

AI Technical Summary

Problems solved by technology

[0003] The existing technology is generally based on the deep learning method to segment blood vessels. The network structure is generally based on the cumulative deepening network structure of the convolutional layer. The image is extracted after each convolutional layer, but the convolution itself is a very time-consuming and memory-intensive process. resource method, and the way the picture is stored will affect the efficiency of the computer when performing related calculation operations on the picture

At the same time, due to the overall similarity of coronary arteries, and the division of blood vessels in medicine is distinguished by different positions and orientations, these two points have caused certain difficulties for the segmentation of coronary arteries

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