Semantic segmentation method based on pyramid cavity convolution network

Abstract

The invention discloses a semantic segmentation method based on a pyramid cavity convolution network, and the method comprises the following steps: obtaining a medical image data set containing a realsegmentation result, and carrying out the preprocessing operation of data enhancement and the like of the data set; processing the preprocessed image through a residual recursion convolution module and a pooling layer to obtain shallow image features; obtaining deep image features through a network in which a pyramid pooling module and a cavity convolution module are connected in parallel; decoding the features of the deep image through a deconvolution layer, jump connection and residual recursion convolution module; inputting a decoding result into a softmax layer to obtain a category to which each pixel belongs; training a pyramid cavity convolution network, establishing a loss function, and determining network parameters through training samples; and inputting a test image into the trained pyramid cavity convolutional network to obtain a semantic segmentation result of the image. According to the method, multi-scale semantic information and detail information can be effectively extracted by adopting a hole convolution and pyramid pooling method, and the segmentation effect of the network is improved.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a semantic segmentation method based on a pyramid hole convolution network. Background technique [0002] In recent years, with the rapid development of deep learning technology, its application in the field of medical image analysis has become more and more extensive. Among them, semantic segmentation technology plays a huge role in various application scenarios such as treatment planning, disease diagnosis, and pathological research. For medical images, to accurately identify the category of each object in the image requires a knowledge background in a professional field, and it takes a certain amount of time for the professional authority. Through the study of semantic segmentation technology, it is possible to automatically and accurately segment the input medical images, so that doctors can make more accurate judgments and design better treatment plans. [0003] Tr...

AI Technical Summary

Problems solved by technology

However, after pooling, the resolution of the feature map of the image will be correspondingly reduced, which will lead to the loss of detail information

Although multi-scale detailed information is obtained through skip connections in the U-Net network, it still leads to the loss of boundary position information and the decline of the model's spatial discrimination ability.

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