CT image segmentation system based on attention convolutional neural network

Abstract

The invention provides a CT image segmentation system based on an attention convolutional neural network, and the system comprises a feature coding module which uses a parallel convolutional neural network to gradually reduce the size of a feature map of an input image, and achieves the simultaneous extraction of image semantic information and spatial information through the multiplexing of a network layer and the interception and fusion of features of all layers; the semantic information extraction attention module which is used for generating attention features by pooling and further refining the semantic information features extracted by the feature coding module; the feature fusion pooling attention module which is used for fusing the refined semantic information features with the semantic information and spatial information features spliced by the feature coding module to form an attention feature map by using parallel connection of maximum pooling and average pooling; and the feature map decoding module which is used for gradually and finely restoring the attention feature map into the size of the input image by using a convolution module and an up-sampling module. Accordingto the invention, by fusing the attention module, efficient and accurate image segmentation is realized.

Description

technical field [0001] The invention relates to the technical field of image understanding, in particular to a CT image segmentation system based on an attentional convolutional neural network. Background technique [0002] Image segmentation is an important basic research issue in the field of computer vision, and medical image segmentation, as an application of image segmentation, can accurately and accurately locate the lesions of a large number of patients in a short time and quickly. Therefore, how to effectively apply image segmentation technology to medical imaging has become the main task of researchers. [0003] Medical image segmentation classifies the semantic expression in the image pixel by pixel through the extraction of medical image features. Medical image segmentation needs to accurately locate the object, the class it belongs to and the location of the object, and it is necessary to clearly divide the object boundary to distinguish different types of objec...

AI Technical Summary

Problems solved by technology

However, there are some defects in the pyramid structure. For example, for the extracted information, what is the part that really needs to be paid attention to by the network, which is not well explained by the pyramid structure.

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