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• W2129948946 abstract "Multi-atlas segmentation has been widely applied in medical image analysis. With deformable registration, this technique realizes label transfer from pre-labeled atlases to unknown images. When deformable registration produces error, label fusion that combines results produced by multiple atlases is an effective way for reducing segmentation errors. Among the existing label fusion strategies, similarity-weighted voting strategies with spatially varying weight distributions have been particularly successful. We show that, weighted voting based label fusion produces a spatial bias that under-segments structures with convex shapes. The bias can be approximated as applying spatial convolution to the ground truth spatial label probability maps, where the convolution kernel combines the distribution of residual registration errors and the function producing similarity-based voting weights. To reduce this bias, we apply a standard spatial deconvolution to the spatial probability maps obtained from weighted voting. In a brain image segmentation experiment, we demonstrate the spatial bias and show that our technique substantially reduces this spatial bias." @default.
• W2129948946 created "2016-06-24" @default.
• W2129948946 creator A5049301997 @default.
• W2129948946 creator A5059550368 @default.
• W2129948946 date "2012-06-01" @default.
• W2129948946 modified "2023-09-24" @default.
• W2129948946 title "Spatial bias in multi-atlas based segmentation" @default.
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• W2129948946 doi "https://doi.org/10.1109/cvpr.2012.6247765" @default.
• W2129948946 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3589983" @default.
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