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- W89621803 abstract "We use object scale and then atom scale Bayesian optimiza- tion of m-reps to automatically segment the caudate nucleus in brain MRI images. Our shape priors are learned after alignment of m-reps fit to 15 manual segmentations of caudates. At the object and atom scale levels the alignment is to the m-rep mean of the object and atom, re- spectively. Our appearance likelihood is learned from regional intensity quantile functions from images that have been aligned, corrected for in- homogeneity, and intensity normalized. We begin the segmentation of a target image by the image preprocessing steps described above followed by an initialization of the mean m-rep model using the image alignment transformation. The segmentation then proceeds by optimizing the pos- terior probability over a shape space encompassing eigenmodes of full m-rep shape variation capturing 87% of the total variance in the training population of left caudates (83% right). The segmentation concludes by successively optimizing the posterior probability of the residual changes in the medial atoms making up the m-rep. Since only a weak variant of a part of our segmentation method was inadvertently applied in the Grand Challenge, its results are not repre- sentative of the performance of our method. Qualitative results of the actual method are reported within; quantitative results will be reported separately." @default.
- W89621803 created "2016-06-24" @default.
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- W89621803 date "2007-01-01" @default.
- W89621803 modified "2023-09-27" @default.
- W89621803 title "Caudate Segmentation using Deformable M-reps" @default.
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