title: Validation study of a fast, accurate, and precise brain tumor volume measurement.
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).
  
  
  CONCLUSIONS
  Our level set is faster on average than MM, yet has accuracy and precision comparable to manual tracing.
authors.affiliation: Imaging Informatics Lab, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada. mdang@ucalgary.ca
authors.forename: Mong
authors.initials: M
authors.lastname: Dang
authors.forename: Jayesh
authors.initials: J
authors.lastname: Modi
authors.forename: Mike
authors.initials: M
authors.lastname: Roberts
authors.forename: Christopher
authors.initials: C
authors.lastname: Chan
authors.forename: J Ross
authors.initials: JR
authors.lastname: Mitchell
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D001921:Brain / Q000473:pathology; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008579:Meningioma / Q000175:diagnosis / Q000473:pathology; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012984:Software
keywords: Brain tumor; Image processing; Magnetic resonance imaging; Segmentation; Volume