Convolutional neural network-based kidney volume estimation from low-dose unenhanced computed tomography scans.

Journal: BMC medical imaging
Published Date: Nov 15, 2023

Abstract

PURPOSE: Kidney volume is important in the management of renal diseases. Unfortunately, the currently available, semi-automated kidney volume determination is time-consuming and prone to errors. Recent advances in its automation are promising but mostly require contrast-enhanced computed tomography (CT) scans. This study aimed at establishing an automated estimation of kidney volume in non-contrast, low-dose CT scans of patients with suspected urolithiasis.

Authors

  • Lukas Müller
    Klinik und Poliklinik für Diagnostische und Interventionelle Radiologie, Universitätsmedizin Mainz, Langenbeckstr. 1, 55131, Mainz, Deutschland. lukas.mueller@unimedizin-mainz.de.
  • Dativa Tibyampansha
    Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center of the Johannes Gutenberg University Mainz, Obere Zahlbacher Str. 69, 55131, Mainz, Germany.
  • Peter Mildenberger
    Klinik und Poliklinik für diagnostische und interventionelle Radiologie, Universitätsmedizin, Johannes-Gutenberg-Universität Mainz, Langenbeckstraße 1, 55131, Mainz, Deutschland.
  • Torsten Panholzer
    Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University Mainz, Germany.
  • Florian Jungmann
    Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany. Electronic address: florian.jungmann@unimedizin-mainz.de.
  • Moritz C Halfmann
    Klinik und Poliklinik für diagnostische und interventionelle Radiologie, Universitätsmedizin, Johannes-Gutenberg-Universität Mainz, Langenbeckstraße 1, 55131, Mainz, Deutschland.

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