Kidney and Renal Tumor Segmentation by nnU-Net Using 3D CT Data from Different Sources

Abstract

In this paper, we present a deep learning-based method for kidney and tumor region segmentation using 3D CT data from multiple sources. We conduct experiments by training with mixed datasets and fine-tuning transfer learning. Throughout these experiments, data augmentation is applied by blending arterial phase CT images and portal vein phase CT images. Our findings reveal a trend: higher accuracy in predicting kidney labels is achieved when fine-tuning transfer learning is applied, while higher accuracy in predicting tumor labels is attained when training with a mixed dataset. This suggests the effectiveness of fine-tuning when the variations in the datasets are relatively small, as seen in the case of kidneys. Conversely, training with mixed datasets proves effective when the variation in prediction targets is relatively large, such as with tumors. It is also confirmed that integrating the results by different training policies improves the overall segmentation results.