Construction of Hierarchical Multi-Organ Statistical Atlases and Their Application to Multi-Organ Segmentation from CT Images

Authors:
Toshiyuki Okada;Keita Yokota;Masatoshi Hori;Masahiko Nakamoto;Hironobu Nakamura;Yoshinobu Sato
Affiliations:
Graduate School of Information Science and Technology, Osaka University, and Division of Image Analysis, ,;Graduate School of Information Science and Technology, Osaka University, and Division of Image Analysis, ,;Department of Radiology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan 565-0871;Division of Image Analysis, , and Graduate School of Information Science and Technology, Osaka University,;Department of Radiology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan 565-0871;Division of Image Analysis, , and Graduate School of Information Science and Technology, Osaka University,
Venue:
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Year:
2008

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Abstract

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.