Wavelet-based double-U-shaped space-frequency fusion transformer network CT image segmentation method

Abstract

The invention relates to a CT image segmentation method based on a wavelet double-U-shaped space-frequency fusion transformer network, and belongs to the field of CT image processing. According to the method, a low-frequency component and a high-frequency component of a feature map are obtained through Haar wavelet transformation, linear self-attention interaction is carried out in low frequency and high frequency respectively, a network learns a global structure from the low frequency and captures detail features in the high frequency, a U-shaped encoder-decoder structure on the outer layer of the network supplements space information for a Transform network, and therefore the network structure is optimized. The local feature extraction capability of the Transform network is enhanced, so that the network can mine local details in the image more deeply, and finally, the spatial domain feature and the frequency domain feature are taken as two branches to realize feature fusion according to space and channel dimension alternate weighting. The method can improve the precision of the segmentation result and the detail retention capability.

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