Multi-feature-based plaque characterization in ex vivo MRI trained by registration to 3D histology
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
Multi-feature-based plaque characterization in ex vivo MRI trained by registration to 3D histology. / Engelen, Arna van; Niessen, Wiro J.; Klein, Stefan; Groen, Harald C. ; Verhagen, Hence J. M.; Wentzel, Jolanda J.; Lugt, Aad van der; de Bruijne, Marleen.
In: Physics in Medicine and Biology, Vol. 57, No. 1, 2012, p. 241-256.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Multi-feature-based plaque characterization in ex vivo MRI trained by registration to 3D histology
AU - Engelen, Arna van
AU - Niessen, Wiro J.
AU - Klein, Stefan
AU - Groen, Harald C.
AU - Verhagen, Hence J. M.
AU - Wentzel, Jolanda J.
AU - Lugt, Aad van der
AU - de Bruijne, Marleen
PY - 2012
Y1 - 2012
N2 - We present a new method for automated characterization of atherosclerotic plaque composition in ex vivo MRI. It uses MRI intensities as well as four other types of features: smoothed, gradient magnitude and Laplacian images at several scales, and the distances to the lumen and outer vessel wall. The ground truth for fibrous, necrotic and calcified tissue was provided by histology and micro-CT in 12 carotid plaque specimens. Semi-automatic registration of a 3D stack of histological slices and micro-CT images to MRI allowed for 3D rotations and inplane deformations of histology. By basing voxelwise classification on different combinations of features, we evaluated their relative importance. To establish whether training by 3D registration yields different results than training by 2D registration, we determined plaque composition using (1) a 2D slice-based registration approach for three manually selected MRI and histology slices per specimen, and (2) an approach that uses only the three corresponding MRI slices from the 3D-registered volumes. Voxelwise classification accuracy was best when all features were used (73.3 ± 6.3 %) and was significantly better than when only original intensities and distance features were used (Friedman, p <0.05). Although 2D registration or selection of three slices from the 3D set slightly decreased accuracy, these differences were non-significant.
AB - We present a new method for automated characterization of atherosclerotic plaque composition in ex vivo MRI. It uses MRI intensities as well as four other types of features: smoothed, gradient magnitude and Laplacian images at several scales, and the distances to the lumen and outer vessel wall. The ground truth for fibrous, necrotic and calcified tissue was provided by histology and micro-CT in 12 carotid plaque specimens. Semi-automatic registration of a 3D stack of histological slices and micro-CT images to MRI allowed for 3D rotations and inplane deformations of histology. By basing voxelwise classification on different combinations of features, we evaluated their relative importance. To establish whether training by 3D registration yields different results than training by 2D registration, we determined plaque composition using (1) a 2D slice-based registration approach for three manually selected MRI and histology slices per specimen, and (2) an approach that uses only the three corresponding MRI slices from the 3D-registered volumes. Voxelwise classification accuracy was best when all features were used (73.3 ± 6.3 %) and was significantly better than when only original intensities and distance features were used (Friedman, p <0.05). Although 2D registration or selection of three slices from the 3D set slightly decreased accuracy, these differences were non-significant.
U2 - 10.1088/0031-9155/57/1/241
DO - 10.1088/0031-9155/57/1/241
M3 - Journal article
C2 - 22156050
VL - 57
SP - 241
EP - 256
JO - Physics in Medicine and Biology
JF - Physics in Medicine and Biology
SN - 0031-9155
IS - 1
ER -
ID: 35458204