Image segmentation method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses an image segmentation method and device, equipment and a storage medium, which can be applied to the field of digital medical treatment. The method comprises the following steps: acquiring a to-be-segmented target image; determining a local image block in the target image and a global image block matched with the local image block in scale; the local image blocks and the global image blocks are input into a preset segmentation network model for image segmentation processing, and the preset segmentation network model is used for performing feature fusion on the input local image blocks and global image blocks and performing segmentation processing based on fused target feature information; and determining a segmentation result corresponding to the target image according to the output of the preset segmentation network model. Through the technical scheme of the embodiment of the invention, the accuracy of image segmentation can be ensured.

Description

technical field [0001] The embodiments of the present invention relate to computer technology, and in particular to an image segmentation method, device, equipment and storage medium. Background technique [0002] With the rapid development of computer technology and deep learning, neural network models can be used to segment high-resolution images. For example, 3D medical images are segmented to reduce the workload of doctors and provide doctors with a quantitative reference for treatment plan formulation and surgical guidance. [0003] At present, the local image blocks in the image can be obtained by cropping the high-resolution image, and the local image blocks are segmented based on the convolutional neural network model. [0004] However, in the course of realizing the present invention, the inventor finds that there are at least the following problems in the prior art: [0005] Due to the limitations of the receptive field of the convolution operator of the convolut...

AI Technical Summary

Problems solved by technology

[0005] Due to the limitations of the receptive field of the convolution operator of the convolutional neural network model, the convolutional neural network model only pays attention to a small number of local features in the image, and cannot obtain global features, especially when dealing with texture, shape and When there are target structures with large differences in size, the accuracy of image segmentation cannot be effectively guaranteed by using the existing convolutional neural network model

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