Collaborative Multi Organ Segmentation by Integrating Deformable and Graphical Models^*

Mustafa Gökhan Uzunba¹, Chao Chen¹, Shaoting Zhang¹, Kilian M. Pohl², Kang Li³, and Dimitris Metaxas¹

¹CBIM, Rutgers University, Piscataway, NJ, USA

²University of Pennsylvania, Philadelphia, PA, USA

³Dept. of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ, USA

Abstract. Organ segmentation is a challenging problem on which significant progress has been made. Deformable models (DM) and graphical models (GM) are two important categories of optimization based image segmentation methods. Efforts have been made on integrating two types of models into one framework. However, previous methods are not designed for segmenting multiple organs simultaneously and accurately. In this paper, we propose a hybrid multi organ segmentation approach by integrating DM and GM in a coupled optimization framework. Specifically, we show that region-based deformable models can be integrated with Markov Random Fields (MRF), such that multiple models’ evolutions are driven by a maximum a posteriori (MAP) inference. It brings global and local deformation constraints into a unified framework for simultaneous segmentation of multiple objects in an image. We validate this proposed method on two challenging problems of multi organ segmentation, and the results are promising.

^*This grant was partially supported based on funding from the following grants NIH-R01-HL086578, NIH-R21-HL088354 and NSF-MRI-1229628.

LNCS 8150, p. 157 ff.

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