A Novel Network With Spectrum Transformer and Triplet Attention for <scp>CT</scp> Image Segmentation

ABSTRACT Deep learning‐based methods have achieved great progress in CT image segmentation in recent years. However, the lack of unified large‐scale datasets, unbalanced categories of segmented images, blurred boundaries between infected and healthy regions, and different sizes and shapes of lesions have led to the fact that the existing methods still have challenges in further improving the segmentation accuracy in medical applications. In this work, a novel network with spectrum Transformer and triplet attention for CT image segmentation is proposed. The computed spectrum Transformer module and the triplet attention module (TAM) are fused in a parallel way utilizing the parallel hybrid fusion module (PHFM), which extracts global and local contextual information from sequence features and cross‐dimensional features. A spectrum Transformer block (STB) is proposed, which utilizes the fast Fourier transform (FFT) to learn the weights of each frequency component in the spectral space. Extensive comparison experiments are conducted on both the COVID‐19‐Seg dataset and Mosmeddata, showing that the proposed network has better accuracy than most existing methods for CT image segmentation tasks. In particular, the proposed model achieves improvement by 1.29% and 2.28% in terms of DSC, respectively, by 1.15% and 1.35% in terms of mIoU. SEN metrics increase by 1.45% and 1.48%, respectively. PRE also achieves the best results, showing its significant advantage in accurately segmenting medical images. SPE also shows quite good results in both datasets. Ablation studies show that the proposed STB and TAM modules improve the segmentation performance significantly.