GBM Volumetry using the 3D Slicer Medical Image Computing Platform

TL;DR

This study demonstrates that using 3D Slicer with GrowCut segmentation significantly reduces time and maintains high accuracy in measuring GBM tumor volumes compared to manual slice-by-slice segmentation.

Contribution

The paper introduces a semi-automated segmentation method in 3D Slicer that improves efficiency while preserving accuracy in GBM volumetry.

Findings

GrowCut segmentation reduces segmentation time by 61%.

Segmentation accuracy with GrowCut achieves an average Dice score of 88.43%.

Variability analysis shows consistent results among physicians.

Abstract

Volumetric change in glioblastoma multiforme (GBM) over time is a critical factor in treatment decisions. Typically, the tumor volume is computed on a slice-by-slice basis using MRI scans obtained at regular intervals. (3D)Slicer - a free platform for biomedical research - provides an alternative to this manual slice-by-slice segmentation process, which is significantly faster and requires less user interaction. In this study, 4 physicians segmented GBMs in 10 patients, once using the competitive region-growing based GrowCut segmentation module of Slicer, and once purely by drawing boundaries completely manually on a slice-by-slice basis. Furthermore, we provide a variability analysis for three physicians for 12 GBMs. The time required for GrowCut segmentation was on an average 61% of the time required for a pure manual segmentation. A comparison of Slicer-based segmentation with manual slice-by-slice segmentation resulted in a Dice Similarity Coefficient of 88.43 +/- 5.23% and a Hausdorff Distance of 2.32 +/- 5.23 mm.