title: Semi-automatic brain tumor segmentation by constrained MRFs using structural trajectories.
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.
authors: Computer Science and Engineering, SUNY at Buffalo, Buffalo, NY, USA. lzhao6@buffalo.edu
authors: Liang
authors: L
authors: Zhao
authors: Wuhan University of Science and Technology, Wuhan, Hubei, China.
authors: Wei
authors: W
authors: Wu
authors: Computer Science and Engineering, SUNY at Buffalo, Buffalo, NY, USA.
authors: Jason J
authors: JJ
authors: Corso
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001932:Brain Neoplasms / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface