DUP-Net: Double U-PoolFormer Networks for Renal Artery Segmentation in CT Urography

Renal artery segmentation plays a fundamental role in nephrectomy, which can help surgeons get a better under-standing of vascular structures. However, the similar intensity between the renal arteries and cortex, the complex variations and tiny structures of arteries, bring challenges to accurate segmentation. To address these issues, we construct double U-PoolFormer networks (DUP-Net) to establish a coarse-to-fine framework for renal artery segmentation. Specifically, we use 2- D U - N et for the kidney extraction and then create 3-D DUP-Net for artery segmentation. DUP-Net is a serial network architecture that uses two U-PoolFormer modules to extract long-range spatial dependencies to create tree-like constraints while removing mis-segmentation of renal cortex through the serial structure. While DUP-Net improving the segmentation accuracy, it reduces memory cost during segmengtation. We evaluated our method on 70 cases of computed tomography urography data, with the experimental results showing that our proposed method certainly outperforms current 2-D and 3-D network models. Particularly, the average dice similarity coefficient of our method was improved from 81.51 % to 88.35%.