Renal tumor segmentation, visualization, and segmentation confidence using ensembles of neural networks in patients undergoing surgical resection.

Journal: European radiology
PMID: 39177855

Abstract

OBJECTIVES: To develop an automatic segmentation model for solid renal tumors on contrast-enhanced CTs and to visualize segmentation with associated confidence to promote clinical applicability.

Authors

  • Sophie Bachanek
    Department of Clinical and Interventional Radiology, University Medical Center Goettingen, Goettingen, Germany.
  • Paul Wuerzberg
    Department of Medical Bioinformatics, University Medical Center Goettingen, Goettingen, Germany.
  • Lorenz Biggemann
    Department of Diagnostic and Interventional Radiology, University Medical Center Goettingen, Goettingen, Germany.
  • Tanja Yani Janssen
    Department of Clinical and Interventional Radiology, University Medical Center Goettingen, Goettingen, Germany.
  • Manuel Nietert
    Department of Medical Bioinformatics, University Medical Center Goettingen, Goettingen, Germany.
  • Joachim Lotz
    1 Department of Diagnostic and Interventional Radiology, University Medical Center Goettingen, Robert Koch Strasse 40, Goettingen 37075, Germany.
  • Philip Zeuschner
    Department of Urology and Pediatric Urology, Saarland University, Homburg/Saar, Germany.
  • Alexander Maßmann
    Department of Radiology and Nuclear Medicine, Robert-Bosch-Clinic, Stuttgart, Germany.
  • Annemarie Uhlig
    2 Department of Urology, University Medical Center Goettingen, Goettingen, Germany.
  • Johannes Uhlig
    1 Department of Diagnostic and Interventional Radiology, University Medical Center Goettingen, Robert Koch Strasse 40, Goettingen 37075, Germany.

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