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A comprehensive testing protocol for MRI neuroanatomical segmentation techniques: Evaluation of a novel lateral ventricle segmentation method.

Although a wide range of approaches have been developed to automatically assess the volume of brain regions from MRI, the reproducibility of these algorithms across different scanners and pulse sequences, their accuracy in different clinical populations and sensitivity to real changes in brain volume have not always been comprehensively examined. Firstly we present a comprehensive testing protocol which comprises 312 freely available MR images to assess the accuracy, reproducibility and sensitivity of automated brain segmentation techniques. Accuracy is assessed in infants, young adults and patients with Alzheimer's disease in comparison to gold standard measures by expert observers using a manual technique based on Cavalieri's principle. The protocol determines the reliability of segmentation between scanning sessions, different MRI pulse sequences and 1.5T and 3T field strengths and examines their sensitivity to small changes in volume using a large longitudinal dataset. Secondly we apply this testing protocol to a novel algorithm for segmenting the lateral ventricles and compare its performance to the widely used FSL FIRST and FreeSurfer methods. The testing protocol produced quantitative measures of accuracy, reliability and sensitivity of lateral ventricle volume estimates for each segmentation method. The novel algorithm showed high accuracy in all populations (intraclass correlation coefficient, ICC>0.95), good reproducibility between MRI pulse sequences (ICC>0.99) and was sensitive to age related changes in longitudinal data. FreeSurfer demonstrated high accuracy (ICC>0.95), good reproducibility (ICC>0.99) and sensitivity whilst FSL FIRST showed good accuracy in young adults and infants (ICC>0.90) and good reproducibility (ICC=0.98), but was unable to segment ventricular volume in patients with Alzheimer's disease or healthy subjects with large ventricles. Using the same computer system, the novel algorithm and FSL FIRST processed a single MRI image in less than 10min while FreeSurfer took approximately 7h. The testing protocol presented enables the accuracy, reproducibility and sensitivity of different algorithms to be compared. We also demonstrate that the novel segmentation algorithm and FreeSurfer are both effective in determining lateral ventricular volume and are well suited for multicentre and longitudinal MRI studies.

Pubmed ID: 21835253

Authors

  • Kempton MJ
  • Underwood TS
  • Brunton S
  • Stylios F
  • Schmechtig A
  • Ettinger U
  • Smith MS
  • Lovestone S
  • Crum WR
  • Frangou S
  • Williams SC
  • Simmons A

Journal

NeuroImage

Publication Data

October 15, 2011

Associated Grants

  • Agency: NIA NIH HHS, Id: P01 AG003991
  • Agency: NIA NIH HHS, Id: P01 AG03991
  • Agency: NIMH NIH HHS, Id: P20 MH071616
  • Agency: NIA NIH HHS, Id: P50 AG005681
  • Agency: NIA NIH HHS, Id: P50 AG05681
  • Agency: NIA NIH HHS, Id: R01 AG021910
  • Agency: NIA NIH HHS, Id: R01 AG021910
  • Agency: NCRR NIH HHS, Id: U24 RR021382
  • Agency: NCRR NIH HHS, Id: U24 RR021382
  • Agency: Wellcome Trust, Id: WT 088641/Z/09/Z

Mesh Terms

  • Adult
  • Aging
  • Algorithms
  • Alzheimer Disease
  • Child, Preschool
  • Databases, Factual
  • Female
  • Humans
  • Image Processing, Computer-Assisted
  • Infant
  • Lateral Ventricles
  • Longitudinal Studies
  • Magnetic Resonance Imaging
  • Male
  • Observer Variation
  • Reference Standards
  • Reproducibility of Results
  • Young Adult