Abstract: Automatic segmentation of kidneys and their tumor areas in CT image sequences can provide quantitative references for radiotherapy and chemotherapy planning.Currently,kidney tumor segmentation models based on Transformer have attracted widespread attention,especially when used in conjunction with the U-Net model and its variants.Existing Transformer-based segmentation networks typically learn features within local windows of individual slices,resulting in insufficient representation zof intra-slice spatial information and inter-slice axial information.To address this issue,a three- dimensional axial Transformer module is proposed,which decomposes the complex coupling of the three dimensions into alternating axial attentions,integrating both intra-slice and inter-slice axial correlation information.Based on the three-dimensional axial Transformer module,a two-stage kidney tumor segmentation encoder-decoder network,ATrans UNet (Axial Transformer UNet),incorporates multi-scale features and residual learning.On KiTS19 dataset,the Dice similarity coefficients for kidney and kidney tumor segmentation are 96.43% and 81.04%,respectively,representing an improvement of 8.40% over 2D-Unet and 4.84% over 3D-Unet in average Dice scores.

Key words: CT image sequences;3D segmentation of kidney tumors;3D axial Transformer, two-stage encoding-decoding network