Context pyramid fusion network and image segmentation method

Abstract

The invention discloses a context pyramid fusion network and an image segmentation method, and the context pyramid fusion network comprises a feature coding module which comprises a plurality of feature extraction layers which are connected step by step, and is used for obtaining a feature map of an original image; a plurality of global pyramid guiding modules, connected with the different featureextraction layers respectively and used for fusing the feature maps extracted by the feature extraction layers connected with the global pyramid guiding modules with the feature maps extracted by allthe higher feature extraction layers to obtain global context information and guiding and transmitting the global context information to the feature decoding module through jump connection; a scale sensing pyramid fusion module, connected with the highest feature extraction layer of the feature coding module and used for dynamically selecting a correct receptive field according to the feature maps of different scales and fusing multi-scale context information; and a feature decoding module, used for reconstructing a feature map according to the global context information and the multi-scale context information. The method is good in image segmentation performance, and is better in effectiveness and universality.

Description

technical field [0001] The invention relates to a context pyramid fusion network and an image segmentation method, belonging to the technical field of image processing. Background technique [0002] Semantic segmentation of medical images is an important step in medical image analysis. Realize lesion region segmentation in different medical images, such as segmentation of skin lesions in dermoscopic images, segmentation of linear lesions in indocyanine green fundus angiography images, segmentation of dangerous organs in chest CT images, and segmentation of retinal optical coherence tomography (OCT). ) image segmentation of macular edema damage, etc., is the basis for quantitative analysis of lesions. However, in the case of generally low imaging resolution of medical images, medical images generally have the characteristics of low contrast, blurred boundaries of lesion areas, etc., coupled with the characteristics of variety and shape diversity of lesions, the semantic segm...

AI Technical Summary

Problems solved by technology

Although the CNN network with U-shaped structure has achieved good performance in medical image segmentation, the context information extraction ability of its single encoder-decoder is still not sufficient.

This will lead to the gradual weakening of the global context information obtained by the deep encoder when it is passed to the shallow decoder step by step

In addition, the simple skip connection between the encoder-decoder at each level ignores the global feature information and non-selectively fuses the local information, which will introduce irrelevant interference information, which will lead to segmentation errors

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