Measurement consistency from magnetic resonance images

In quantifying medical images, length-based measurements are still obtained manually. Due to possible human error, a measurement protocol is required to guarantee the consistency of measurements. In this work, we review various statistical techniques that can be used in determining measurement consistency. The focus is on detecting a possible measurement bias and determining the robustness of the procedures to outliers.

We review correlation analysis, linear regression, Bland-Altman method, paired t-test, and analysis of variance (ANOVA). These techniques were applied to measurements, obtained by two raters, of head and neck structures from magnetic resonance images.

The correlation analysis and the linear regression were shown to be insufficient for detecting measurement inconsistency. They are also very sensitive to outliers. The widely used Bland-Altman method is a visualization technique, so it lacks the numeric quantification. The paired t-test tends to be sensitive to small measurement bias. In contrast, ANOVA performs well even under small measurement bias.

In almost all cases, using only one method is insufficient and it is recommended that several methods be used simultaneously. In general, ANOVA performs the best.