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- W2804247377 abstract "Deformable registration based multi-atlas segmentation has been successfully applied in a broad range of anatomy segmentation applications. However, the excellent performance comes with a high computational burden due to the requirement for deformable image registration and voxel-wise label fusion. To address this problem, we investigate the role of corrective learning (Wang et al., 2011) in speeding up multi-atlas segmentation. We propose to combine multi-atlas segmentation with corrective learning in a multi-scale analysis fashion for faster speeds. First, multi-atlas segmentation is applied in a low spatial resolution. After resampling the segmentation result back to the native image space, learning-based error correction is applied to correct systematic errors due to performing multi-atlas segmentation in a low spatial resolution. In cardiac CT and brain MR segmentation experiments, we show that applying multi-atlas segmentation in a coarse scale followed by learning-based error correction in the native space can substantially reduce the overall computational cost, with only modest or no sacrificing segmentation accuracy." @default.
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- W2804247377 date "2018-09-01" @default.
- W2804247377 modified "2023-10-16" @default.
- W2804247377 title "Fast anatomy segmentation by combining coarse scale multi-atlas label fusion with fine scale corrective learning" @default.
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- W2804247377 doi "https://doi.org/10.1016/j.compmedimag.2018.05.002" @default.
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