Renal Tumor Segmentation in CT using Cascade U-Net with 2.5D approach

We propose Cascade U-Net with 2.5D approach to segment kidney and tumor from 3D CT image. We use standard U-Net to generate segmentations per each volume slide (2D image). 4 prediction volumes are generated per different magnification and slice direction. Then, consolidate the volumes to formulate the final prediction volume. Per experiment on the KiTS19 dataset, we get a 12% raise in dice coefficient when compare with single U-Net prediction