Validation study of a fast, accurate, and precise brain tumor volume measurement

Mong Dang, Jayesh Modi, Mike Roberts, Christopher Chan, J. Ross Mitchell

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

Precision and accuracy are sometimes sacrificed to ensure that medical image processing is rapid. To address this, our lab had developed a novel level set segmentation algorithm that is 16× faster and >96% accurate on realistic brain phantoms. Methods: This study reports speed, precision and estimated accuracy of our algorithm when measuring MRIs of meningioma brain tumors and compares it to manual tracing and modified MacDonald (MM) ellipsoid criteria. A repeated-measures study allowed us to determine measurement precisions (MPs) - clinically relevant thresholds for statistically significant change. Results: Speed: the level set, MM, and trace methods required 1:20, 1:35, and 9:35 (mm:ss) respectively on average to complete a volume measurement (p<0.05). Accuracy: the level set was not statistically different to the estimated true lesion volumes (p>0.05). Precision: the MM's within-operator and between-operator MPs were significantly higher (worse) than the other methods (p<0.05). The observed difference in MP between the level set and trace methods did not reach statistical significance (p>0.05). Conclusion: Our level set is faster on average than MM, yet has accuracy and precision comparable to manual tracing.

Original languageEnglish (US)
Pages (from-to)480-487
Number of pages8
JournalComputer Methods and Programs in Biomedicine
Volume111
Issue number2
DOIs
StatePublished - Aug 1 2013

Keywords

  • Brain tumor
  • Image processing
  • Magnetic resonance imaging
  • Segmentation
  • Volume

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics

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