Plaque characterization in ex vivo MRI evaluated by dense 3D correspondence with histology
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Plaque characterization in ex vivo MRI evaluated by dense 3D correspondence with histology. / van Engelen, Arna; de Bruijne, Marleen; Klein, Stefan; Verhagen, Hence; Groen, Harold; Wentzel, Jolanda; van der Lugt, Aad; Niessen, Wiro.
Medical Imaging 2011: computer-aided diagnosis. ed. / Ronald M. Summers; Bram van Ginneken. Vol. Part One SPIE - International Society for Optical Engineering, 2011. 796329 (Progress in Biomedical Optics and Imaging; No. 32, Vol. 12).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Plaque characterization in ex vivo MRI evaluated by dense 3D correspondence with histology
AU - van Engelen, Arna
AU - de Bruijne, Marleen
AU - Klein, Stefan
AU - Verhagen, Hence
AU - Groen, Harold
AU - Wentzel, Jolanda
AU - van der Lugt, Aad
AU - Niessen, Wiro
PY - 2011
Y1 - 2011
N2 - Automatic quantification of carotid artery plaque composition is important in the development of methods that distinguish vulnerable from stable plaques. MRI has shown to be capable of imaging different components noninvasively. We present a new plaque classification method which uses 3D registration of histology data with ex vivo MRI data, using non-rigid registration, both for training and evaluation. This is more objective than previously presented methods, as it eliminates selection bias that is introduced when 2D MRI slices are manually matched to histological slices before evaluation. Histological slices of human atherosclerotic plaques were manually segmented into necrotic core, fibrous tissue and calcification. Classification of these three components was voxelwise evaluated. As features the intensity, gradient magnitude and Laplacian in four MRI sequences after different degrees of Gaussian smoothing, and the distances to the lumen and the outer vessel wall, were used. Performance of linear and quadratic discriminant classifiers for different combinations of features was evaluated. Best accuracy (72.5 ± 7.7%) was reached with the linear classifier when all features were used. Although this was only a minor improvement to the accuracy of a classifier that only included the intensities and distance features (71.6 ± 7.9%), the difference was statistically significant (paired t-test, p
AB - Automatic quantification of carotid artery plaque composition is important in the development of methods that distinguish vulnerable from stable plaques. MRI has shown to be capable of imaging different components noninvasively. We present a new plaque classification method which uses 3D registration of histology data with ex vivo MRI data, using non-rigid registration, both for training and evaluation. This is more objective than previously presented methods, as it eliminates selection bias that is introduced when 2D MRI slices are manually matched to histological slices before evaluation. Histological slices of human atherosclerotic plaques were manually segmented into necrotic core, fibrous tissue and calcification. Classification of these three components was voxelwise evaluated. As features the intensity, gradient magnitude and Laplacian in four MRI sequences after different degrees of Gaussian smoothing, and the distances to the lumen and the outer vessel wall, were used. Performance of linear and quadratic discriminant classifiers for different combinations of features was evaluated. Best accuracy (72.5 ± 7.7%) was reached with the linear classifier when all features were used. Although this was only a minor improvement to the accuracy of a classifier that only included the intensities and distance features (71.6 ± 7.9%), the difference was statistically significant (paired t-test, p
U2 - 10.1117/12.878007
DO - 10.1117/12.878007
M3 - Article in proceedings
VL - Part One
T3 - Progress in Biomedical Optics and Imaging
BT - Medical Imaging 2011
A2 - Summers, Ronald M.
A2 - van Ginneken, Bram
PB - SPIE - International Society for Optical Engineering
T2 - Medical Imaging 2011
Y2 - 15 February 2011 through 15 February 2011
ER -
ID: 40276527