Medical image automatic segmentation method based on multi-path attention fusion

Abstract

The invention belongs to the technical field of medical image processing and computer vision, in particular to a medical image automatic segmentation method based on multi-path attention fusion, whichcomprises the following steps: obtaining a medical image data set, dividing the data set into a training set and a verification set, augmenting images in the training set, and normalizing images of the verification set and the augmented images in the training set; inputting the pictures in the training set into a multi-path attention fusion network model, and outputting under the guidance of a cross entropy loss function to obtain a segmentation result picture; selecting a model with the highest accuracy of the verification set, inputting the test set into a multi-path attention fusion network loading the model, and outputting to obtain a segmentation result graph of the image. According to the method, the problems that in the medical image segmentation process, an existing network cannoteffectively improve the feature quality under different scales through an encoder, interlayer dependence between network low-level structure features and high-level semantic features is difficult tocontrol, and consequently the segmentation result is poor are solved.

Description

Technical field [0001] The invention belongs to the technical field of medical image processing and computer vision, and particularly relates to an automatic segmentation method for medical images based on multi-path attention fusion. Background technique [0002] Medical images play a key role in medical treatment and diagnosis. The goal of a computer-aided diagnosis (CAD) system is to provide doctors with accurate interpretation of medical images, so that a large number of patients can be better treated. Moreover, the automatic processing of medical images leads to a reduction in the time, cost and errors of human-based processing. One of the main research areas in this field is medical image segmentation, which is a key step in many medical imaging research. [0003] Like other search fields in computer vision, deep learning networks have achieved excellent results and outperform non-deep technologies in medical imaging. Deep neural networks are mainly used for classification...

AI Technical Summary

Problems solved by technology

However, in the hierarchical transformation in the U-Net network, the learning process of different pooling levels often shares the same data path, so the generated multi-scale feature map may not be fully distinguished as expected. Due to the existence of the pooling layer, the encoder Losing part of the spatial information, the single U-Net network uses simple two consecutive 3×3 convolutional layers. This standard convolutional layer is difficult to preserve rich spatial information. Another disadvantage is that the U-Net network uses It is a simple skip connection, which only splices the features of the encoder and the features of the decoder, and the features output from the decoder will be redundant, which will affect the final segmentation result

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