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Abdominal Multi-organ CT Segmentation Using Organ Correlation Graph and Prediction-Based Shape and Location Priors
Toshiyuki Okada1, Marius George Linguraru2, Masatoshi Hori1, Ronald M. Summers3, Noriyuki Tomiyama1, and Yoshinobu Sato1
1Department of Radiology, Graduate School of Medicine Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
toshi@image.med.osaka-u.ac.jp
2Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Medical Center, Washington DC, USA
3National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, 10 Center Drive Bethesda, MD 20892, USA
Abstract. The paper addresses the automated segmentation of multiple organs in upper abdominal CT data. We propose a framework of multi-organ segmentation which is adaptable to any imaging conditions without using intensity information in manually traced training data. The features of the framework are as follows: (1) the organ correlation graph (OCG) is introduced, which encodes the spatial correlations among organs inherent in human anatomy; (2) the patient-specific organ shape and location priors obtained using OCG enable the estimation of intensity priors from only target data and optionally a number of untraced CT data of the same imaging condition as the target data. The proposed methods were evaluated through segmentation of eight abdominal organs (liver, spleen, left and right kidney, pancreas, gallbladder, aorta, and inferior vena cava) from 86 CT data obtained by four imaging conditions at two hospitals. The performance was comparable to the state-of-the-art method using intensity priors constructed from manually traced data.
Keywords: shape prediction, intensity model, partial least squares
LNCS 8151, p. 275 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013