Construction of hierarchical multi-organ statistical atlases and their application to multi-organ segmentation from CT images.
Hierarchical multi-organ statistical atlases are constructed with the aim of achieving fully automated segmentation of the liver and related organs from computed tomography images. Constraints on inter-relations among organs are embedded in hierarchical organization of probabilistic atlases (PAs) and statistical shape models (SSMs). Hierarchical PAs are constructed based on the hierarchical nature of inter-organ relationships. Multi-organ SSMs (MO-SSMs) are combined with previously proposed single-organ multi-level SSMs (ML-SSMs). A hierarchical segmentation procedure is then formulated using the constructed hierarchical atlases. The basic approach consists of hierarchical recursive processes of initial region extraction using PAs and subsequent refinement using ML/MO-SSMs. The experimental results show that segmentation accuracy of the liver was improved by incorporating constraints on inter-organ relationships.
Okada
Toshiyuki
T
Graduate School of Information Science and Technology, Osaka University, Japan.
Yokota
Keita
K
Hori
Masatoshi
M
Nakamoto
Masahiko
M
Nakamura
Hironobu
H
Sato
Yoshinobu
Y
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011860:Radiography, Abdominal / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014781:Viscera / Q000000981:diagnostic imaging*