Relaxed conditional statistical shape models and their application to non-contrast liver segmentation

Authors:
Sho Tomoshige;Elco Oost;Akinobu Shimizu;Hidefumi Watanabe;Hidefumi Kobatake;Shigeru Nawano
Affiliations:
Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Koganei-shi, Tokyo, Japan;Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Koganei-shi, Tokyo, Japan;Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Koganei-shi, Tokyo, Japan;Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Koganei-shi, Tokyo, Japan;Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Koganei-shi, Tokyo, Japan;Department of Radiology, International University of Health and Welfare, Mita Hospital, Minato-ku, Tokyo, Japan
Venue:
MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications
Year:
2012

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Abstract

This paper proposes a novel conditional statistical shape model (SSM) that allows a relaxed conditional term. The method is based on the selection formula and allows a seamless transition between the non-conditional SSM and the conventional conditional SSM. Unlike a conventional conditional SSM, the relaxed conditional SSM can take the reliability of the condition into account. Organ shapes estimated by the proposed SSM were used as shape priors for Graph Cut based segmentation. Results for liver shape estimation and subsequent liver segmentation show the benefit of the proposed model over conventional conditional SSMs.