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GBM volumetry using the 3D Slicer medical image computing platform.

Jan Egger1, Tina Kapur, Andriy Fedorov

  • 1Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. egger@bwh.harvard.edu

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Automated glioblastoma segmentation using 3D Slicer's GrowCut is 61% faster than manual methods. This AI-powered approach offers comparable accuracy for treatment planning.

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Area of Science:

  • Neuro-oncology
  • Medical Imaging Analysis
  • Computational Biology

Background:

  • Accurate volumetric assessment of glioblastoma multiforme (GBM) is crucial for treatment planning and monitoring.
  • Traditional manual segmentation of GBM from MRI scans is time-consuming and labor-intensive.
  • The development of automated or semi-automated segmentation tools can improve efficiency and consistency.

Purpose of the Study:

  • To evaluate the efficiency and accuracy of the GrowCut segmentation module in 3D Slicer for GBM volume measurement.
  • To compare the performance of 3D Slicer's GrowCut segmentation with manual slice-by-slice segmentation.
  • To analyze inter-physician variability in GBM segmentation.

Main Methods:

  • Four physicians performed GBM segmentation on 10 patients using both 3D Slicer's GrowCut and manual slice-by-slice methods.
  • A variability analysis was conducted for three physicians across 12 GBM cases.
  • Quantitative metrics including Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD) were used for comparison.

Main Results:

  • GrowCut segmentation required, on average, 61% of the time compared to pure manual segmentation.
  • The Slicer-based segmentation achieved a Dice Similarity Coefficient of 88.43 ± 5.23% against manual segmentation.
  • The Hausdorff Distance between Slicer-based and manual segmentations was 2.32 ± 5.23 mm.

Conclusions:

  • 3D Slicer's GrowCut segmentation offers a significantly faster and efficient alternative to manual GBM segmentation.
  • The GrowCut module provides accurate and reproducible results comparable to manual methods, suitable for clinical decision-making.
  • Automated segmentation tools like GrowCut can streamline the workflow for neuro-oncology image analysis.