Deep Learning-Based Automated Kidney and Cortex Segmentation from Non-contrast T1-weighted Images

Motivation: In the realm of kidney imaging, the precise measurement of kidney volumes, including total, cortical, and medullary volumes, is of significant clinical importance, but manual segmentation is time-consuming and impractical. Goal(s): To develop a fully automated deep learning-based segmentation method for segmenting the entire kidney and internal structures in MR images. Approach: Utilized a 3D nnU-Net deep learning model trained with non-contrast-enhanced T1-weight MR images from 40 volunteers, validated against manual segmentation. Results: The automated method strongly correlated with manual measurements (Pearson’s > 0.9) and achieved Dice coefficients of 0.96 for the whole kidney and 0.84 for the cortex on the test set. Impact: This deep learning approach offers rapid, precise, and replicable kidney volume analysis, enhancing both research and clinical care.