Cross-validation of brain segmentation by SPM5 and SIENAX

Abstract 

Volumes of cerebral grey (GM) or white matter (WM) are often used as clinical observations or statistical covariates. Several automated segmentation tools can be used for this purpose, but they have not been validated against each other. We used the most common ones, SPM5 and SIENAX 2.4, to derive volumes of grey and white matter in 56 healthy subjects (mean age 49±13, range 22–80) and compared the two methods. Both methods yielded significant correlations with age in the expected directions, and estimates of parenchymal volumes were highly correlated. However, without use of prior probability maps, or priors, in SIENAX, GM was significantly underestimated in comparison to SPM (0.52±.06 vs 0.66±.07 L) and WM was significantly overestimated (0.48±.07 vs 0.46±.07 L). This error was associated with misclassification of GM as cerebrospinal fluid, especially in deep grey matter. Invoking prior probabilities in SIENAX resulted in excellent agreement with SPM: GM and WM volumes were found to be 0.64±0.07 L and 0.47±0.07 L, respectively. We conclude that SIENAX requires priors for accurate volumetric estimates, and then provides close agreement with SPM5.

Keywords: MRI, Grey matter, White matter, Segmentation, SPM, SIENAX