TY - JOUR
T1 - A comprehensive testing protocol for MRI neuroanatomical segmentation techniques
T2 - Evaluation of a novel lateral ventricle segmentation method
AU - Kempton, Matthew J.
AU - Underwood, Tracy S.A.
AU - Brunton, Simon
AU - Stylios, Floris
AU - Schmechtig, Anne
AU - Ettinger, Ulrich
AU - Smith, Marcus S.
AU - Lovestone, Simon
AU - Crum, William R.
AU - Frangou, Sophia
AU - Williams, Steven C.R.
AU - Simmons, Andrew
N1 - Funding Information:
The authors acknowledge financial support from the National Institute for Health Research (NIHR) Specialist Biomedical Research Centre for Mental Health award to the South London and Maudsley NHS Foundation Trust and the Institute of Psychiatry, King's College London. W.R. Crum acknowledges support from the King's College London Centre of Excellence in Medical Engineering funded by the Wellcome Trust and EPSRC ( WT 088641/Z/09/Z ). We are grateful to the Open Access Structural Imaging Series for the use of this data and include their following grant numbers: P50 AG05681 , P01 AG03991 , R01 AG021910 , P20 MH071616 , and U24 RR021382 . Ulrich Ettinger acknowledges support from the Deutsche Forschungsgemeinschaft ( ET 31/2-1 ).
PY - 2011/10/15
Y1 - 2011/10/15
N2 - 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 1.5. T and 3. T 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 10. min while FreeSurfer took approximately 7. h. 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.
AB - 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 1.5. T and 3. T 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 10. min while FreeSurfer took approximately 7. h. 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.
KW - Lateral ventricles
KW - MRI reliability
KW - Segmentation
KW - Sensitivity
UR - https://www.scopus.com/pages/publications/80052616108
U2 - 10.1016/j.neuroimage.2011.06.080
DO - 10.1016/j.neuroimage.2011.06.080
M3 - Article
C2 - 21835253
AN - SCOPUS:80052616108
SN - 1053-8119
VL - 58
SP - 1051
EP - 1059
JO - NeuroImage
JF - NeuroImage
IS - 4
ER -