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Proceedings Paper

Asymmetric bias in user guided segmentations of brain structures
Author(s): Martin Styner; Rachel G. Smith; Michael M. Graves; Matthew W. Mosconi; Sarah Peterson; Scott White; Joe Blocher; Mohammed El-Sayed; Heather C. Hazlett
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Paper Abstract

Brain morphometric studies often incorporate comparative asymmetry analyses of left and right hemispheric brain structures. In this work we show evidence that common methods of user guided structural segmentation exhibit strong left-right asymmetric biases and thus fundamentally influence any left-right asymmetry analyses. We studied several structural segmentation methods with varying degree of user interaction from pure manual outlining to nearly fully automatic procedures. The methods were applied to MR images and their corresponding left-right mirrored images from an adult and a pediatric study. Several expert raters performed the segmentations of all structures. The asymmetric segmentation bias is assessed by comparing the left-right volumetric asymmetry in the original and mirrored datasets, as well as by testing each sides volumetric differences to a zero mean standard t-tests. The structural segmentations of caudate, putamen, globus pallidus, amygdala and hippocampus showed a highly significant asymmetric bias using methods with considerable manual outlining or landmark placement. Only the lateral ventricle segmentation revealed no asymmetric bias due to the high degree of automation and a high intensity contrast on its boundary. Our segmentation methods have been adapted in that they are applied to only one of the hemispheres in an image and its left-right mirrored image. Our work suggests that existing studies of hemispheric asymmetry without similar precautions should be interpreted in a new, skeptical light. Evidence of an asymmetric segmentation bias is novel and unknown to the imaging community. This result seems less surprising to the visual perception community and its likely cause is differences in perception of oppositely curved 3D structures.

Paper Details

Date Published: 3 March 2007
PDF: 8 pages
Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65120K (3 March 2007); doi: 10.1117/12.709049
Show Author Affiliations
Martin Styner, Univ. of North Carolina at Chapel Hill (United States)
Rachel G. Smith, Univ. of North Carolina at Chapel Hill (United States)
Michael M. Graves, Univ. of North Carolina at Chapel Hill (United States)
Matthew W. Mosconi, Univ. of North Carolina at Chapel Hill (United States)
Sarah Peterson, Univ. of North Carolina at Chapel Hill (United States)
Scott White, Univ. of North Carolina at Chapel Hill (United States)
Joe Blocher, Univ. of North Carolina at Chapel Hill (United States)
Mohammed El-Sayed, Mansoura Univ. (Egypt)
Heather C. Hazlett, Univ. of North Carolina at Chapel Hill (United States)


Published in SPIE Proceedings Vol. 6512:
Medical Imaging 2007: Image Processing
Josien P. W. Pluim; Joseph M. Reinhardt, Editor(s)

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