TY - JOUR
T1 - Efficient multilevel brain tumor segmentation with integrated bayesian model classification
AU - Corso, Jason J.
AU - Sharon, Eitan
AU - Dube, Shishir
AU - El-Saden, Suzie
AU - Sinha, Usha
AU - Yuille, Alan
PY - 2008/5
Y1 - 2008/5
N2 - 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.
AB - 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.
KW - Bayesian affinity
KW - Brain tumor
KW - Glioblastoma multiforme
KW - Multilevel segmentation
KW - Normalized cuts
UR - http://www.scopus.com/inward/record.url?scp=43049179622&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=43049179622&partnerID=8YFLogxK
U2 - 10.1109/TMI.2007.912817
DO - 10.1109/TMI.2007.912817
M3 - Article
C2 - 18450536
AN - SCOPUS:43049179622
SN - 0278-0062
VL - 27
SP - 629
EP - 640
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 5
M1 - 4384610
ER -