XXXX rates in @entity195 : automated segmentation variability analysis.

@entity1 @entity131 @entity26946 @entity73 @entity194 @entity195
@entity131 ( @entity131 ) @entity194 and @entity194 rates are putative clinical markers of progression to @entity195 ( @entity195 ). We compared results given by two different automated @entity131 segmentation techniques in the @entity195 Neuroimaging Initiative dataset between two time intervals. We used @entity131 volumetric automated segmentation data for a total of 683 @entity1 at baseline (198 controls, 331 with @entity73 ( @entity73 ) and 154 with @entity195 ), 684 at 6 months (198 controls, 332 with @entity73 and 154 with @entity195 ) and 587 at 12 months (176 controls, 280 with @entity73 and 131 with @entity195 ). Segmentation techniques included FreeSurfer and @entity26946 . We calculated @entity131 monthly @entity194 rates between baseline and 6 months and between 6 and 12 months, and used a multiple-way ANOVA for repeated measures. Mean @entity131 volumes decrease with time. The only significant (p<0.05) main effect was diagnosis. We measured strong interaction between technique and scan interval and weak interaction between diagnoses and scan interval. When compared to mean rates from largely manual segmentation, automated segmentation results show increased @entity194 rates for both @entity26946 and FreeSurfer techniques. While sensitive, there remains substantial technique variability, likely due to differences in methodological approaches and especially @entity131 . These fundamental metrological problems need to be resolved before concluding with certainty on the accuracy and reliability of automated techniques.
