Kidney segmentation is often necessary for analyzing renal MRI data. Deep learning approach shows much promise, but typically requires large numbers of images for model training. In this study, we explored the feasibility of segmenting MRI images using ResUNet pre-trained with CT images and fine-tuning with transfer learning. The fine-tuning step used 60 MRI images (from 5 subjects) only. The trained model performed excellently in segmenting an independent set of MRI images, with DICE similarity of 0.94 and volume error of 13%±9%. This study demonstrates the power of transfer learning in utilizing images of a different modality in kidney segmentation.