Medical image segmentation method and device and storage medium

Abstract

The embodiment of the invention discloses a medical image segmentation method and device and a storage medium. The method comprises the steps of obtaining a to-be-segmented medical image; carrying outfeature extraction on the to-be-segmented medical image through a residual network; obtaining a high-level feature map and a plurality of low-level feature maps; then, on one hand, different receptive fields are adopted to identify the high-level feature map; obtaining local feature information, on the other hand, the correlation between each pixel point in the high-level feature map and the high-level feature map is calculated; then, fusing the local feature information with the global feature information, and segmenting a target area and a target object in the to-be-segmented medical imagebased on the fused high-level feature map and the plurality of low-level feature maps; According to the scheme, the detail segmentation accuracy can be improved, and the overall segmentation effect ofthe medical image is improved.

Description

technical field [0001] The present invention relates to the field of communication technology, in particular to a medical image segmentation method, device and storage medium. Background technique [0002] With the development of artificial intelligence (AI, Artificial Intelligence), the application of AI in the medical field is becoming more and more extensive. Taking medical image segmentation as an example, the convolutional neural network is generally used to extract the high-level features of the image to be segmented, and then the pyramid scene analysis network (PSPNet, Pyramid Scene ParsingNetwork) and image segmentation model (DeepLab) are used to respectively The extracted high-level features use pooling operations (Pooling) and Atrous Convolution (Atrous Convolution) operations of different scales to fuse high-level features and low-level features, and accordingly cut out the target object from the image to be segmented, such as from Liver cancer regions are segme...

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Problems solved by technology

[0003] However, during the research and practice of the existing technology, the inventors of the present invention found that the high-level features extracted by the existing scheme have very limited feature expression ability, which leads to low accuracy of detail segmentation and greatly affects the quality of medical images. The overall segmentation effect of

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