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Semi-automatic Brain Tumor Segmentation by Constrained MRFs Using Structural Trajectories*
Liang Zhao1, Wei Wu2, and Jason J. Corso1
1Computer Science and Engineering, SUNY at Buffalo, Buffalo, NY, USA
2Wuhan University of Science and Technology, Wuhan, Hubei, China
Abstract. Quantifying volume and growth of a brain tumor is a primary prognostic measure and hence has received much attention in the medical imaging community. Most methods have sought a fully automatic segmentation, but the variability in shape and appearance of brain tumor has limited their success and further adoption in the clinic. In reaction, we present a semi-automatic brain tumor segmentation framework for multi-channel magnetic resonance (MR) images. This framework does not require prior model construction and only requires manual labels on one automatically selected slice. All other slices are labeled by an iterative multi-label Markov random field optimization with hard constraints. Structural trajectories—the medical image analog to optical flow—and 3D image over-segmentation are used to capture pixel correspondences between consecutive slices for pixel labeling. We show robustness and effectiveness through an evaluation on the 2012 MICCAI BRATS Challenge Dataset; our results indicate superior performance to baselines and demonstrate the utility of the constrained MRF formulation.
*This work was partially supported by the Chinese National Science Foundation (61273241) and the NSF CAREER grant IIS-0845282.
LNCS 8151, p. 567 ff.
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© Springer-Verlag Berlin Heidelberg 2013