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Published in:
2013 | OriginalPaper | Chapter
Collaborative Multi Organ Segmentation by Integrating Deformable and Graphical Models
Authors : Mustafa Gökhan Uzunbaş, Chao Chen, Shaoting Zhang, Kilian M. Pohl, Kang Li, Dimitris Metaxas
Published in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013
Publisher: Springer Berlin Heidelberg
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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.