Multi-modal glioblastoma segmentation: man versus machine

PLoS One. 2014 May 7;9(5):e96873. doi: 10.1371/journal.pone.0096873. eCollection 2014.

Abstract

Background and purpose: Reproducible segmentation of brain tumors on magnetic resonance images is an important clinical need. This study was designed to evaluate the reliability of a novel fully automated segmentation tool for brain tumor image analysis in comparison to manually defined tumor segmentations.

Methods: We prospectively evaluated preoperative MR Images from 25 glioblastoma patients. Two independent expert raters performed manual segmentations. Automatic segmentations were performed using the Brain Tumor Image Analysis software (BraTumIA). In order to study the different tumor compartments, the complete tumor volume TV (enhancing part plus non-enhancing part plus necrotic core of the tumor), the TV+ (TV plus edema) and the contrast enhancing tumor volume CETV were identified. We quantified the overlap between manual and automated segmentation by calculation of diameter measurements as well as the Dice coefficients, the positive predictive values, sensitivity, relative volume error and absolute volume error.

Results: Comparison of automated versus manual extraction of 2-dimensional diameter measurements showed no significant difference (p = 0.29). Comparison of automated versus manual segmentation of volumetric segmentations showed significant differences for TV+ and TV (p<0.05) but no significant differences for CETV (p>0.05) with regard to the Dice overlap coefficients. Spearman's rank correlation coefficients (ρ) of TV+, TV and CETV showed highly significant correlations between automatic and manual segmentations. Tumor localization did not influence the accuracy of segmentation.

Conclusions: In summary, we demonstrated that BraTumIA supports radiologists and clinicians by providing accurate measures of cross-sectional diameter-based tumor extensions. The automated volume measurements were comparable to manual tumor delineation for CETV tumor volumes, and outperformed inter-rater variability for overlap and sensitivity.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Brain Neoplasms / diagnostic imaging*
  • Brain Neoplasms / pathology
  • Contrast Media
  • Female
  • Glioblastoma / diagnostic imaging*
  • Glioblastoma / pathology
  • Humans
  • Image Interpretation, Computer-Assisted*
  • Magnetic Resonance Imaging / methods*
  • Male
  • Middle Aged
  • Pattern Recognition, Automated
  • Radiography
  • Software
  • Tumor Burden

Substances

  • Contrast Media

Grant support

This study was supported by the Swiss National Science Foundation (http://p3.snf.ch/Project-140958), the Bernese Cancer League and the Swiss Cancer League. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.