Aperture NeuroNeurosciencePeer-reviewed journal article
Visual QC Protocol for FreeSurfer Cortical Parcellations from Anatomical MRI
Pradeep Reddy RaamanaiD, Athena E. TheyersiD, Tharushan Selliah, Piali BhatiiD, Stephen R. ArnottiD, Stefanie HasseliD, Nuwan Dayananda NanayakkaraiD, Christopher J.M. ScottiD, Jacqueline K. HarrisiD, Mojdeh ZamyadiiD, Raymond Wing Moon LamiD, Roumen V. MileviD, Daniel J. MülleriD, Susan RotzingeriD, Benicio Noronha FreyiD, Sidney H. KennedyiD, Sandra E. BlackiD, Anthony Edward LangiD, Mario MasellisiD, Sean P. SymonsiD, Robert BarthaiD, GLENDA M. MacQUEENiD, The CAN-BIND Investigator Team, the ONDRI Investigators, Stephen C. StrotheriD
Abstract
Quality control of morphometric neuroimaging data is essential to improve reproducibility. Owing to the complexity of neuroimaging data and subsequently the interpretation of their results, visual inspection by trained raters is the most reliable way to perform quality control. Here, we present a protocol for visual quality control of the anatomical accuracy of FreeSurfer parcellations, based on an easy-to-use open source tool called VisualQC. We comprehensively evaluate its utility in terms of error detection rate and inter-rater reliability on two large multi-site datasets, and discuss site differences in error patterns. This evaluation shows that VisualQC is a practically viable protocol for community adoption.
- assay: imaging
- research focus: benchmarking
- research focus: deconvolution
- research focus: spatial domain detection
- approach: statistical model
- approach: benchmark study
- Citations
- 24
- FWCI
- 1.23
- Field percentile
- top 21%
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