Computer-Aided Diagnosis

Manually segmented template library for 8-year-old pediatric brain MRI data with 16 subcortical structures

[+] Author Affiliations
Amanmeet Garg, Karteek Popuri, Mirza Faisal Beg

Simon Fraser University, School of Engineering Science, Burnaby, British Columbia V5A 1M4, Canada

Darren Wong, Kenneth J. Poskitt

University of British Columbia, Department of Radiology, Vancouver, British Columbia V5Z 1M9, Canada

Kevin Fitzpatrick, Bruce Bjornson, Ruth E. Grunau

University of British Columbia, Department of Pediatrics, Vancouver, British Columbia V6H 3V4, Canada

Child and Family Research Institute, Vancouver, British Columbia V5Z 4H4, Canada

J. Med. Imag. 1(3), 034502 (Oct 28, 2014). doi:10.1117/1.JMI.1.3.034502
History: Received March 18, 2014; Revised September 15, 2014; Accepted September 17, 2014
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Abstract.  Manual segmentation of anatomy in brain MRI data taken to be the closest to the “gold standard” in quality is often used in automated registration-based segmentation paradigms for transfer of template labels onto the unlabeled MRI images. This study presents a library of template data with 16 subcortical structures in the central brain area which were manually labeled for MRI data from 22 children (8 male, mean age=8±0.6years). The lateral ventricle, thalamus, caudate, putamen, hippocampus, cerebellum, third vevntricle, fourth ventricle, brainstem, and corpuscallosum were segmented by two expert raters. Cross-validation experiments with randomized template subset selection were conducted to test for their ability to accurately segment MRI data under an automated segmentation pipeline. A high value of the dice similarity coefficient (0.86±0.06, min=0.74, max=0.96) and small Hausdorff distance (3.33±4.24, min=0.63, max=25.24) of the automated segmentation against the manual labels was obtained on this template library data. Additionally, comparison with segmentation obtained from adult templates showed significant improvement in accuracy with the use of an age-matched library in this cohort. A manually delineated pediatric template library such as the one described here could provide a useful benchmark for testing segmentation algorithms.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Amanmeet Garg ; Darren Wong ; Karteek Popuri ; Kenneth J. Poskitt ; Kevin Fitzpatrick, et al.
"Manually segmented template library for 8-year-old pediatric brain MRI data with 16 subcortical structures", J. Med. Imag. 1(3), 034502 (Oct 28, 2014). ; http://dx.doi.org/10.1117/1.JMI.1.3.034502


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