A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans.

Journal: Medical physics
PMID: 35156216

Abstract

PURPOSE: Early detection and size quantification of renal calculi are important for optimizing treatment and preventing severe kidney stone disease. Prior work has shown that volumetric measurements of kidney stones are more informative and reproducible than linear measurements. Deep learning-based systems that use abdominal noncontrast computed tomography (CT) scans may assist in detection and reduce workload by removing the need for manual stone volume measurement. Prior to this work, no such system had been developed for use on noisy low-dose CT or tested on a large-scale external dataset.

Authors

  • Daniel C Elton
    Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bldg. 10 Room 1C224D MSC 1182, Bethesda, MD 20892-1182.
  • Evrim B Turkbey
    Imaging Biomarkers and Computer-aided Diagnosis Laboratory and Clinical Image Processing Service, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, 20892-1182, USA.
  • Perry J Pickhardt
    University of Wisconsin Medical School, Department of Radiology, Madison, Wisconsin, United States.
  • Ronald M Summers
    National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, 10 Center Drive, Bethesda, MD 20892, USA.

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