TY - GEN
T1 - Collaborative multi organ segmentation by integrating deformable and graphical models
AU - Uzunbaş, Mustafa Gökhan
AU - Chen, Chao
AU - Zhang, Shaoting
AU - Pohl, Kilian M.
AU - Li, Kang
AU - Metaxas, Dimitris
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84885916960
U2 - 10.1007/978-3-642-40763-5_20
DO - 10.1007/978-3-642-40763-5_20
M3 - Conference contribution
C2 - 24579136
AN - SCOPUS:84885916960
SN - 9783642407628
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 157
EP - 164
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
T2 - 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Y2 - 22 September 2013 through 26 September 2013
ER -