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Updated: Jul 20, 2025

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
FreeSurfer 7 quality control: Key problem areas and importance of manual corrections
Vesa Vahermaa1, Dogu Baran Aydogan2, Tuukka Raij3
1Department of Computer Science, Aalto School of Science, Aalto University, Finland; Mental Health Unit, Finnish Institute for Health and Welfare, Finland.
Abstract:
We have studied the effects of manual quality control of brain Magnetic Resonance Imaging (MRI) images processed with Freesurfer. T1 images of first episode psychosis patients (N = 60) and healthy controls (N = 41) were inspected for gray matter boundary errors. The errors were fixed, and the effects of error correction on brain volume, thickness, and surface area were measured. It is commonplace to apply quality control to Freesurfer MRI recordings to ensure that the edges of gray and white matter are detected properly, as incorrect edge detection leads to changes in variables such as volume, cortical thickness, and cortical surface area. We find that while Freesurfer v7.1.1. does regularly make mistakes in identifying the edges of cortical gray matter, correcting these errors yields limited changes in the commonly measured variables listed above. We further find that the software makes fewer gray matter boundary errors when processing female brains. The results suggest that manually correcting gray matter boundary errors may not be worthwhile due to its small effect on the measurements, with potential exceptions for studies that focus on the areas that are more commonly affected by errors: the areas around the cerebellar tentorium, paracentral lobule, and the optic nerves, specifically the horizontal segment of the middle cerebral artery.
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