2.5D Cascaded Semantic Segmentation for Kidney Tumor Cyst

Zhiwei Chen, Hanqiang Liu

Keywords: Multi-stage, Kidney and tumor and cyst segmentation, Deep Learning

Abstract: Accurate segmentation of kidney tumours can help doctors diagnose the disease. In this work, we described a multi-stage 2.5D semantic segmentation networks to automatically segment kidney and tumor and cyst in computed tomography (CT) images. First, the kidney is pre-segmented by the first stage network ResSENormUnet; then, the kidney and the tumor and cyst are fine-segmented by the second stage network DenseTransUnet, and finally, a post-processing operation based on a 3D connected region is used for the removal of false-positive segmentation results. We evaluate this approach in the KiTS21 challenge, which shows promising performance.