Segmentation of Kidney Tumors in CT Images Based on the RAScU ‐Net Network

ABSTRACT Renal CT images contain complex spatial volumetric information, and deep learning models have difficulty learning consistent features from images with different voxel spacing, resulting in suboptimal segmentation performance for kidney tumors. We have introduced a modified deep learning model called RAScU‐Net to address these challenges. The RAScU‐Net adopts a coarse‐to‐fine segmentation strategy and a novel voxel spacing unification method to segment renal regions while preserving CT image data. The residual asymmetric self‐calibration (RASc) and residual self‐calibration (ResSc) modules are designed to capture feature information from CT images with different voxel spacings, thereby establishing long‐range dependencies. Finally, the innovative Channel Attention Gate (CAG) module generates attention maps from multiple perspectives, facilitating the integration of high‐ and low‐level features. The experimental results demonstrate that the RAScU‐Net significantly surpasses existing kidney tumor segmentation networks, achieving Dice scores of 0.882 and 0.877 for tumor segmentation of KiTS19 and KiTS21, respectively.