Identifying errors in Freesurfer automated skull stripping and the incremental utility of manual intervention.
Quality assurance (QA) is vital for ensuring the integrity of processed neuroimaging data for use in clinical neurosciences research. Manual QA (visual inspection) of processed brains for cortical surface reconstruction errors is resource-intensive, particularly with large datasets. Several semi-aut...
| Publicado en: | Brain Imaging & Behavior Vol. 13; no. 5; pp. 1281 - 1292 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=138504747&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138504747 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Oct2019 vid: 13 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138504747 138504747 143924899 NLM30145718 10.1007/s11682-018-9951-8 NLM30145718 138504747 ppf: 1281 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identifying errors in Freesurfer automated skull stripping and the incremental utility of manual intervention. aug: au: Waters, Abigail B. Mace, Ryan A. Sawyer, Kayle S. Gansler, David A. affil: Department of Psychology, Suffolk University, 73 Tremont Street, Boston, MA, USA sug: subj: Brain Anatomy and Histology Software Image Processing, Computer Assisted Magnetic Resonance Imaging Adult Male Middle Age Neuroradiography Female Ferrans and Powers Quality of Life Index Scales Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Quality assurance (QA) is vital for ensuring the integrity of processed neuroimaging data for use in clinical neurosciences research. Manual QA (visual inspection) of processed brains for cortical surface reconstruction errors is resource-intensive, particularly with large datasets. Several semi-automated QA tools use quantitative detection of subjects for editing based on outlier brain regions. There were two project goals: (1) evaluate the assumption that statistical outliers are related to errors of cortical extension, and (2) examine whether error identification and correction significantly impacts estimation of cortical parameters and established brain-behavior relationships. T1 MPRAGE images (N = 530) of healthy adults were obtained from the NKI-Rockland Sample and reconstructed using Freesurfer 5.3. Visual inspection of T1 images was conducted for: (1) participants (n = 110) with outlier values (z scores ±3 SD) for subcortical and cortical segmentation volumes (outlier group), and (2) a random sample of remaining participants (n = 110) with segmentation values that did not meet the outlier criterion (non-outlier group). The outlier group had 21% more participants with visual inspection-identified errors than participants in the non-outlier group, with a medium effect size (Φ = 0.22). Nevertheless, a considerable portion of images with errors of cortical extension were found in the non-outlier group (41%). Although nine brain regions significantly changed size from pre- to post-editing (with effect sizes ranging from 0.26 to 0.59), editing did not substantially change the correlations of neurocognitive tasks and brain volumes (ps > 0.05). Statistically-based QA, although less resource intensive, is not accurate enough to supplant visual inspection. We discuss practical implications of our findings to guide resource allocation decisions for image processing. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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