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Updated: Jul 10, 2025

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
Reliability of automated brain volumetric analysis: A test by comparing NeuroQuant and volBrain software
Panagiotis Koussis1,2, Panagiotis Toulas1, Demetrios Glotsos2
1Bioiatriki SA/MRI Department, Kifissias ave and Papada, Athens, Greece.
Background And Purpose:
Brain volume analysis from magnetic resonance imaging (MRI) is gaining an important role in neurological diagnosis. This study compares the volumes of brain segments measured by two automated brain analysis software, NeuroQuant (NQ), and volBrain (VB) in order to test their reliability in brain volumetry.
Methods:
Using NQ and VB software, the same brain segment volumes were calculated and compared, taken from 56 patients scanned under the same MRI sequence. These segments were intracranial cavity, putamen, thalamus, amygdala, whole brain, cerebellum, white matter, and hippocampus. The paired t-test method has been used to determine if there was a significant difference in these measurements. The interclass correlation (ICC) is used to test inter-method reliability between the two software. Finally, regression analysis was used to examine the possibility of linear correlation.
Results:
In all brain segments tested but hippocampus, significant differences were found. ICC presents satisfactory to excellent reliability in all brain segments except thalamus and amygdala for which reliability has been proven to be poor. In most cases, linear correlation was found.
Conclusions:
The significant differences found in the majority of the tested brain segments are raising questions about the reliability of automated brain analysis as a quantitative tool. Strong linear correlation of the volumetric measurements and good reliability indicates that, each software provides good qualitative information of brain structures size.
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