Blood vessel segmentation method and device

Abstract

The invention provides a blood vessel segmentation method and device. The method comprises the following steps: inputting a CTA image into a first convolutional network, and performing blood vessel pre-segmentation based on multi-scale feature extraction; constructing a blood vessel distribution diagram; inputting the CTA image into a second convolutional network, multiplying the output of the second convolutional network by the input CTA image, and inputting the multiplied result into a third convolutional network; enabling the third convolutional network and a graph convolutional network to perform feature interaction based on bidirectional mapping, perform global feature modeling based on a blood vessel distribution graph to capture multi-scale local and global features, and realize blood vessel region prediction; and fusing the multi-scale features to predict a blood vessel segmentation result. According to the invention, blood vessel segmentation is carried out based on cross-network multi-scale feature fusion and local feature and global feature fusion, and compared with blood vessel segmentation based on local features in the prior art, the method can effectively prevent the model from predicting a segmentation region which does not conform to a blood vessel structure, thereby improving the accuracy of blood vessel segmentation.

Description

technical field [0001] The invention belongs to the technical field of medical imaging blood vessel segmentation, and in particular relates to a blood vessel segmentation method and device. Background technique [0002] Common angiographic techniques have been widely used in clinical diagnosis and treatment. The segmentation algorithm can realize automatic reconstruction of blood vessels (such as head and neck vessels, coronary arteries, etc.), which greatly improves the operating efficiency of the hospital while reducing the work pressure of technicians. In the actual scene, some external factors (such as artifacts, noise, shooting technology, etc.) will affect the quality of blood vessel imaging, so that the results of the segmentation algorithm may be missed or broken. [0003] The existing blood vessel segmentation method mainly predicts the blood vessel area through the extraction of local features, and cannot model the overall structure of the blood vessel; the existi...

AI Technical Summary

Problems solved by technology

In the actual scene, some external factors (such as artifacts, noise, shooting technology, etc.) will affect the quality of blood vessel imaging, so that the results of the segmentation algorithm are missing or broken

[0003] The existing blood vessel segmentation method mainly predicts the blood vessel area through the extraction of local features, and cannot model the overall structure of the blood vessel; the existing method mainly uses multi-scale local features for blood vessel prediction, and cannot predict the global structure of multiple scales. feature learning and combining

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