부산대학교 도서관

Volumetric measurements are needed to characterize kidney structural findings on CT images to evaluate and test their potential utility in clinical decision making. Deep learning can enable this task in a scalable and reliable manner. Although automated kidney segmentation has been previously explored, methods for distinguishing cortex from medulla have never been done before. In addition, automated methods are typically evaluated at a single institution, without testing generalizability and robustness across different institutions. The tool developed in this study performs at the level of human readers and could enable large diverse population studies to evaluate how kidney, cortex, and medulla volumes can be used in various clinical settings, and establish normative values at large scale.