RT Journal Article
SR Electronic
T1 Evaluating the Effects of White Matter Multiple Sclerosis Lesions on the Volume Estimation of 6 Brain Tissue Segmentation Methods
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 1109
OP 1115
DO 10.3174/ajnr.A4262
VO 36
IS 6
A1 S. Valverde
A1 A. Oliver
A1 Y. Díez
A1 M. Cabezas
A1 J.C. Vilanova
A1 L. Ramió-Torrentà
A1 À. Rovira
A1 X. Lladó
YR 2015
UL http://www.ajnr.org/content/36/6/1109.abstract
AB BACKGROUND AND PURPOSE: The accuracy of automatic tissue segmentation methods can be affected by the presence of hypointense white matter lesions during the tissue segmentation process. Our aim was to evaluate the impact of MS white matter lesions on the brain tissue measurements of 6 well-known segmentation techniques. These include straightforward techniques such as Artificial Neural Network and fuzzy C-means as well as more advanced techniques such as the Fuzzy And Noise Tolerant Adaptive Segmentation Method, fMRI of the Brain Automated Segmentation Tool, SPM5, and SPM8.MATERIALS AND METHODS: Thirty T1-weighted images from patients with MS from 3 different scanners were segmented twice, first including white matter lesions and then masking the lesions before segmentation and relabeling as WM afterward. The differences in total tissue volume and tissue volume outside the lesion regions were computed between the images by using the 2 methodologies.RESULTS: Total gray matter volume was overestimated by all methods when lesion volume increased. The tissue volume outside the lesion regions was also affected by white matter lesions with differences up to 20 cm3 on images with a high lesion load (≈50 cm3). SPM8 and Fuzzy And Noise Tolerant Adaptive Segmentation Method were the methods less influenced by white matter lesions, whereas the effect of white matter lesions was more prominent on fuzzy C-means and the fMRI of the Brain Automated Segmentation Tool.CONCLUSIONS: Although lesions were removed after segmentation to avoid their impact on tissue segmentation, the methods still overestimated GM tissue in most cases. This finding is especially relevant because on images with high lesion load, this bias will most likely distort actual tissue atrophy measurements.ANNArtificial Neural NetworkFANTASMFuzzy And Noise Tolerant Adaptive Segmentation MethodFASTFMRIB Automated Segmentation ToolFCMfuzzy C-meansH1Hospital Vall d'Hebron, Barcelona, SpainH2Hospital Universitari Dr. Josep Trueta, Girona, SpainH3Clinica Girona, Girona, SpainWMLwhite matter lesion