A Cascaded 3D Segmentation Model for Renal Enhanced CT Images

In order to compete in the KiTS21 challenge, we propose a 3D deep learning cascaded model for the renal enhanced CT image segmentation. The proposed model comprises two stages, where stage 1 segments the kidney and stage 2 segments the tumor and cyst. The proposed deep learning network architecture is based on the residual and 3D UNet architecture. The designed network is utilized for each segmentation stage (for stage 1 and stage 2). Our intended cascaded model achieved a dice score of 0.96 for the kidney, 0.81 for the tumor, and 0.45 for the cyst on the KiTS21 validation dataset.