Method and a device for segmenting a blood vessel wall and a blood flow area in a blood vessel OCT image

Abstract

The embodiment of the invention provides a method and a device for segmenting a blood vessel wall and a blood flow area in a blood vessel OCT image, and the method comprises the steps: segmenting a contour map of the blood vessel wall by utilizing a first-stage full convolutional neural network in a cascade full convolutional neural network model based on a blood vessel Doppler OCT intensity map;and based on the contour map of the blood vessel wall, carrying out background noise removal processing on the blood vessel Doppler OCT phase map, and based on the processed blood vessel Doppler OCT phase map, segmenting the contour map of the blood flow region by using a second-stage full convolutional neural network in a cascade full convolutional neural network model. According to the embodiment of the invention, the cascade full convolution network is used for Doppler OCT blood vessel feature extraction, the segmentation of the blood flow area is more accurate in combination with the Doppler signal, the segmentation difficulty of the blood vessel wall and the blood flow area in the blood vessel OCT image can be effectively reduced, and the blood vessel wall and the blood flow area in the image can be conveniently and effectively segmented.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of image processing, and more specifically, to a method and device for segmenting blood vessel walls and blood flow regions in a blood vessel OCT image. Background technique [0002] Optical coherence tomography (OCT) is an imaging technique with micron-level spatial resolution and millimeter-level imaging depth, which matches the characteristic size of vascular anastomosis. 3D imaging. In addition, Doppler OCT can simultaneously provide three-dimensional structure and blood flow information of blood vessels. [0003] At present, there are many biomedical image segmentation technologies based on deep learning. When the Doppler OCT system is used to obtain mouse arterial images, due to the blood scattering in the blood vessels, and as the depth increases, the sensitivity of the system decreases and the image quality Severe noise, low contrast and other factors, especially the blurred l...

AI Technical Summary

Problems solved by technology

[0003] At present, there are many biomedical image segmentation technologies based on deep learning. When the Doppler OCT system is used to obtain mouse arterial images, due to the blood scattering in the blood vessels, and as the depth increases, the sensitivity of the system decreases, and the image is blurred. Severe noise, low contrast and other factors, especially the blurred lower edge of the vessel wall in the vessel intensity map, make it difficult to completely and accurately segment the vessel wall, and a large amount of background noise appears in the phase map, making it difficult to accurately segment the blood flow area

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