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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Okada, T., Yokota, K., Hori, M., Nakamoto, M., Nakamura, H., & Sato, Y. (2008). Construction of hierarchical multi-organ statistical atlases and their application to multi-organ segmentation from CT images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5241 LNCS, pp. 502–509). https://doi.org/10.1007/978-3-540-85988-8_60
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