title: Asymmetric bias in user guided segmentations of brain structures.
abstract: Brain morphometric studies often incorporate comparative hemispheric asymmetry analyses of segmented brain structures. In this work, we present evidence that common user guided structural segmentation techniques exhibit strong left-right asymmetric biases and thus fundamentally influence any left-right asymmetry analyses. In this study, MRI scans from ten pediatric subjects were employed for studying segmentations of amygdala, globus pallidus, putamen, caudate, and lateral ventricle. Additionally, two pediatric and three adult scans were used for studying hippocampus segmentation. Segmentations of the sub-cortical structures were performed by skilled raters using standard manual and semi-automated methods. The left-right mirrored versions of each image were included in the data and segmented in a random order to assess potential left-right asymmetric bias. Using shape analysis we further assessed whether the asymmetric bias is consistent across subjects and raters with the focus on the hippocampus. The user guided segmentation techniques on the sub-cortical structures exhibited left-right asymmetric volume bias with the hippocampus displaying the most significant asymmetry values (p<<0.01). The hippocampal shape analysis revealed the bias to be strongest on the lateral side of the body and medial side of the head and tail. The origin of this asymmetric bias is considered to be based in laterality of visual perception; therefore segmentations with any degree of user interaction contain an asymmetric bias. The aim of our study is to raise awareness in the neuroimaging community regarding the presence of the asymmetric bias and its influence on any left-right hemispheric analyses. We also recommend reexamining previous research results in the light of this new finding.
authors: Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA. emaltbie@email.unc.edu
authors: Eric
authors: E
authors: Maltbie
authors: Kshamta
authors: K
authors: Bhatt
authors: Beatriz
authors: B
authors: Paniagua
authors: Rachel G
authors: RG
authors: Smith
authors: Michael M
authors: MM
authors: Graves
authors: Matthew W
authors: MW
authors: Mosconi
authors: Sarah
authors: S
authors: Peterson
authors: Scott
authors: S
authors: White
authors: Joseph
authors: J
authors: Blocher
authors: Mohammed
authors: M
authors: El-Sayed
authors: Heather C
authors: HC
authors: Hazlett
authors: Martin A
authors: MA
authors: Styner
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001321:Autistic Disorder / Q000473:pathology*; D001921:Brain / Q000473:pathology*; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity