Automatic Segmentation of Vestibular Schwannoma From MRI Using Two Cascaded Deep Learning Networks.

Journal: The Laryngoscope
PMID: 39744768

Abstract

OBJECTIVE: Automatic segmentation and detection of vestibular schwannoma (VS) in MRI by deep learning is an upcoming topic. However, deep learning faces generalization challenges due to tumor variability even though measurements and segmentation of VS are essential for growth monitoring and treatment planning. Therefore, we introduce a novel model combining two Convolutional Neural Network (CNN) models for the detection of VS by deep learning aiming to improve performance of automatic segmentation.

Authors

  • Sophia Marie Häußler
    Department of Otorhinolaryngology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Christian S Betz
    Department of Otorhinolaryngology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Marta Della Seta
    Institute of Radiology, Charité-Universitätsmedizin Berlin, Berlin Humboldt Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.
  • Dennis Eggert
    Clinic and Polyclinic for Otolaryngology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Alexander Schlaefer
    Institute of Medical Technology, Hamburg University of Technology, Hamburg, Germany. schlaefer@tuhh.de.
  • Debayan Bhattacharya
    Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology, 21073, Hamburg, Germany.

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