Medical image segmentation method of residual full convolutional neural network based on attention mechanism

Abstract

The invention provides a medical image segmentation method of a residual full convolutional neural network based on an attention mechanism. The medical image segmentation method comprises the steps: preprocessing a to-be-segmented medical image; constructing a residual full convolutional neural network based on the attention mechanism, wherein the residual full convolutional neural network comprises a feature map contraction network, an attention network and a feature map expansion network group; inputting the training set data into a residual error type full convolutional neural network for training to obtain a learned convolutional neural network model; and inputting the test set data into the learned convolutional neural network model, and performing image segmentation to obtain segmented images. According to the medical image segmentation method, an attention network is utilized to effectively transmit image features extracted from a feature map contraction network to a feature mapexpansion network; and the problem of lack of image spatial features in an image deconvolution process is solved while the attention network can also inhibit image regions irrelevant to a segmentation target in a low-layer feature image, so that the redundancy of the image is reduced, and meanwhile, the accuracy of image segmentation is also improved.

Description

technical field [0001] The invention relates to a medical image segmentation method of a residual type fully convolutional neural network based on an attention mechanism. Background technique [0002] Medical image segmentation is a key issue that determines whether medical images can provide reliable evidence in clinical diagnosis and treatment. The development of medical image segmentation technology not only affects the development of other related technologies in medical image processing, such as visualization, 3D reconstruction, etc., but also plays an extremely important role in the analysis of biomedical images. In recent years, due to the application of deep learning algorithms in medical image segmentation, medical image segmentation technology has made remarkable progress. Medical image segmentation is generally modeled as a pixel-level multi-classification problem, where the goal is to classify each pixel of an image into one of several predefined classes. [00...

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to provide a medical image segmentation method based on a residual type fully convolutional neural network based on an attention mechanism, which solves the problem in the prior art that the spatial detail information is lost during the image deconvolution process, resulting in a lack of Spatial features of images, which simultaneously lead to the problem of excessive and redundant usage of computing resources and model parameters

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