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CT-Based Glioma Segmentation Using Deep Learning: Validation for Emergency Neuro-oncological Care.

Zohal Alnour Ahmed Emam1, Emel Ada2, Berrin Çavuşoğlu1

  • 1Department of Medical Physics, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey.

Journal of Imaging Informatics in Medicine
|April 13, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning model accurately segments diffuse gliomas on CT scans, offering a vital tool for emergency neuro-oncology when MRI is unavailable. This glioma segmentation network (GSN) shows promising results for resource-limited settings.

Keywords:
Brain neoplasmsDeep learningGliomaImage processing computer-assistedTomography X-ray computed

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Area of Science:

  • Neuro-oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate diffuse glioma delineation is critical in emergency neuro-oncology.
  • Magnetic Resonance Imaging (MRI) is often unavailable in time-sensitive situations.
  • Non-contrast Computed Tomography (CT) is a more accessible imaging modality.

Purpose of the Study:

  • To develop and evaluate a deep learning segmentation approach for diffuse gliomas using routine non-contrast CT.
  • To assess the technical feasibility and WHO grade-stratified performance of the model.
  • To demonstrate its potential as a decision-support tool in resource-limited clinical settings.

Main Methods:

  • A retrospective collection of 206 adult diffuse glioma CT scans from a single center.
  • Development of a glioma segmentation network (GSN) using a ResNet-18 encoder and U-Net decoder.
  • Training with inverse-frequency weighted cross-entropy and five-fold cross-validation on a development cohort (n=177) and validation on an independent cohort (n=29).

Main Results:

  • The GSN achieved Dice Similarity Coefficients (DSC) of 0.846 (grade 2), 0.806 (grade 3), and 0.802 (grade 4) in the independent validation cohort.
  • Corresponding 95th percentile Hausdorff Distance (HD95) values were 13.677 mm, 16.193 mm, and 18.776 mm, respectively.
  • Inference throughput reached 20 slices per second, demonstrating rapid processing.

Conclusions:

  • The proposed GSN demonstrates robust performance and clinically meaningful segmentation accuracy on independent CT data.
  • This supports the technical feasibility of CT-based automated glioma delineation for emergency and resource-constrained settings.
  • Prospective multi-center validation is recommended before routine clinical implementation.