Abstract #2995

Deep learning based kidney segmentation for high temporal resolution tracking renal size changes during sequential gas challenges

Kaixuan Zhao^1,2, Joao dos Santos Periquito³, Thomas Gladytz², Kathleen Cantow³, Luis Hummel³, Jason Millward², Sonia Waiczies², Erdmann Seeliger³, Yanqiu Feng¹, and Thoralf Niendorf^2,4

¹School of Biomedical Engineering, Southern Medical University, Guangzhou, China, ²Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbruck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany, ³Institute of Physiology, Charité - Universitätsmedizin Berlin, Berlin, Germany, ⁴Experimental and Clinical Research Center, a joint cooperation between the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany

Fast renal volume changes during sequential gas challenges might indicate the dynamic balance between renal filtration and reabsorption. In the present work, a deep learning based semantic segmentation method is employed to monitor renal size changes.

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