title: Evaluating the effects of white matter multiple sclerosis lesions on the volume estimation of 6 brain tissue segmentation methods.
abstract: OBJECTIVE
  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.
  
  
  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 cm(3) on images with a high lesion load (≈50 cm(3)). 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.
authors: From the Computer Vision and Robotics Group (S.V., A.O., Y.D., X.L.), University of Girona, Campus Montilivi, Girona, Spain svalverde@eia.udg.edu.
authors: S
authors: S
authors: Valverde
authors: From the Computer Vision and Robotics Group (S.V., A.O., Y.D., X.L.), University of Girona, Campus Montilivi, Girona, Spain.
authors: A
authors: http://orcid.org/0000-0002-0115-0647
authors: A
authors: Oliver
authors: From the Computer Vision and Robotics Group (S.V., A.O., Y.D., X.L.), University of Girona, Campus Montilivi, Girona, Spain.
authors: Y
authors: Y
authors: Díez
authors: Magnetic Resonance Unit (M.C., A.R.), Department of Radiology, Vall d'Hebron University Hospital, Barcelona, Spain.
authors: M
authors: M
authors: Cabezas
authors: Girona Magnetic Resonance Center (J.C.V.), Girona, Spain.
authors: J C
authors: JC
authors: Vilanova
authors: Multiple Sclerosis and Neuroimmunology Unit (L.R.-T.), Dr. Josep Trueta University Hospital, Institut d'Investigació Biomèdica de Girona, Girona, Spain.
authors: L
authors: L
authors: Ramió-Torrentà
authors: Magnetic Resonance Unit (M.C., A.R.), Department of Radiology, Vall d'Hebron University Hospital, Barcelona, Spain.
authors: À
authors: À
authors: Rovira
authors: From the Computer Vision and Robotics Group (S.V., A.O., Y.D., X.L.), University of Girona, Campus Montilivi, Girona, Spain.
authors: X
authors: X
authors: Lladó
mesh_terms: D001284:Atrophy / Q000175:diagnosis / Q000473:pathology; D001921:Brain / Q000473:pathology*; D066128:Gray Matter / Q000473:pathology; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000175:diagnosis* / Q000473:pathology*; D009929:Organ Size / Q000502:physiology; D012680:Sensitivity and Specificity; D012984:Software; D066127:White Matter / Q000473:pathology*