Abdominal multi-organ segmentation of CT images based on hierarchical spatial modeling of organ interrelations

Authors:
Toshiyuki Okada;Yasuhide Yoshida;Masatoshi Hori;Ronald M. Summers;Yen-Wei Chen;Noriyuki Tomiyama;Yoshinobu Sato
Affiliations:
Department of Radiology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan;Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan;Department of Radiology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan;Radiology and Imaging Sciences, National Institutes of Health, Clinical Center, Bethesda, MD;Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan;Department of Radiology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan;Department of Radiology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
Venue:
MICCAI'11 Proceedings of the Third international conference on Abdominal Imaging: computational and Clinical Applications
Year:
2011

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Abstract

The automated segmentation of multiple organs in CT data of the upper abdomen is addressed. In order to explicitly incorporate the spatial interrelations among organs, we propose a method for finding and representing the interrelations based on canonical correlation analysis. Furthermore, methods are developed for constructing and utilizing the statistical atlas in which inter-organ constraints are explicitly incorporated to improve accuracy of multi-organ segmentation. The proposed methods were tested to perform segmentation of seven abdominal organs (liver, spleen, kidneys, pancreas, gallbladder and inferior vena cava) from contrast-enhanced CT datasets and was compared to a previous approach. 28 datasets acquired at two institutions were used for the validation. Significant accuracy improvement was observed for the segmentation of pancreas and gallbladder while there was no accuracy reduction for any organ.