2008
DOI: 10.1109/tmi.2007.912817
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Abstract: Abstract-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 a… Show more

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Cited by 369 publications
(223 citation statements)
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References 32 publications
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“…Segmenting brain tumors in magnetic resonance (MR) images involves classifying each voxel as tumor or non-tumor [1,2,3]. This task, a prerequisite for treating brain cancer using radiation therapy, is typically done by hand by expert medical doctors, who find this process laborious and time-consuming.…”
Section: Introductionmentioning
confidence: 99%
“…Segmenting brain tumors in magnetic resonance (MR) images involves classifying each voxel as tumor or non-tumor [1,2,3]. This task, a prerequisite for treating brain cancer using radiation therapy, is typically done by hand by expert medical doctors, who find this process laborious and time-consuming.…”
Section: Introductionmentioning
confidence: 99%
“…4.2) on various benchmark and new, challenging unconstrained videos. To explore the generality of UES, we apply it to supervoxel hierarchies generated by two different methods, GBH [16] as implemented in [33] and SWA [27] as implemented in [9]. For GBH, we construct a supervoxel tree directly from its output supervoxel hierarchy, since the method itself generates a tree structure.…”
Section: Methodsmentioning
confidence: 99%
“…For example, the Bayesian Model used by Corso et al was for the purpose of detecting and segmenting brain tumor from adjacent edema in multichannel MR 3D scans [20]. This Bayesian model was demonstrated to be computationally efficient with a segmentation accuracy of up to 88% compared to earlier studies analyzing MR images [21][22][23][24].…”
Section: Literature Reviewmentioning
confidence: 99%