Efficient multilevel brain tumor segmentation with integrated bayesian model classification.
We present a new method for automatic segmentation of heterogeneous image data that takes a step toward bridging the gap between bottom-up affinity-based segmentation methods and top-down generative model based approaches. The main contribution of the paper is a Bayesian formulation for incorporating soft model assignments into the calculation of affinities, which are conventionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm, and apply the technique to the task of detecting and segmenting brain tumor and edema in multichannel magnetic resonance (MR) volumes. The computationally efficient method runs orders of magnitude faster than current state-of-the-art techniques giving comparable or improved results. Our quantitative results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of glioblastoma multiforme brain tumor.
Corso
J J
JJ
Department of Radiological Sciences, University of California-Los Angeles, Los Angeles, CA 90095, USA. jcorso@cse.buffalo.edu
Sharon
E
E
Dube
S
S
El-Saden
S
S
Sinha
U
U
Yuille
A
A
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001932:Brain Neoplasms / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018511:Systems Integration