Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence
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.
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