Automated segmentation of the liver from 3D CT images using probabilistic atlas and multilevel statistical shape model.
OBJECTIVE
An atlas-based automated liver segmentation method from three-dimensional computed tomographic (3D CT) images has been developed. The method uses two types of atlases, a probabilistic atlas (PA) and a statistical shape model (SSM).


METHODS
Voxel-based segmentation with a PA is first performed to obtain a liver region, then the obtained region is used as the initial region for subsequent SSM fitting to 3D CT images. To improve reconstruction accuracy, particularly for highly deformed livers, we use a multilevel SSM (ML-SSM). In ML-SSM, the entire shape is divided into patches, with principal component analysis applied to each patch. To avoid inconsistency among patches, we introduce a new constraint called the "adhesiveness constraint" for overlapping regions among patches.


RESULTS
The PA and ML-SSM were constructed from 20 training datasets. We applied the proposed method to eight evaluation datasets. On average, volumetric overlap of 89.2 +/- 1.4% and average distance of 1.36 +/- 0.19 mm were obtained.


CONCLUSIONS
The proposed method was shown to improve segmentation accuracy for datasets including highly deformed livers. We demonstrated that segmentation accuracy is improved using the initial region obtained with PA and the introduced constraint for ML-SSM.
Okada
Toshiyuki
T
Department of Computer Science, Osaka University Graduate School of Information Science and Technology, Suita, Osaka, Japan.
Shimada
Ryuji
R
Hori
Masatoshi
M
Nakamoto
Masahiko
M
Chen
Yen-Wei
YW
Nakamura
Hironobu
H
Sato
Yoshinobu
Y
D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods* / Q000706:statistics & numerical data*; D008099:Liver / Q000000981:diagnostic imaging*; D008107:Liver Diseases / Q000000981:diagnostic imaging*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D025341:Principal Component Analysis / Q000379:methods; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*