Fast anatomy segmentation by combining coarse scale multi-atlas label fusion with fine scale corrective learning

被引:10
作者
Wang, Hongzhi [1 ]
Kakrania, Deepika [1 ]
Tang, Hui [1 ]
Prasanna, Prasanth [1 ]
Syeda-Mahmood, Tanveer [1 ]
机构
[1] IBM Almaden Res Ctr, 650 Harry Rd, San Jose, CA 94120 USA
关键词
Multi-atlas segmentation; Corrective learning; Image registration; Label fusion; IMAGE SEGMENTATION; STRATEGIES; SELECTION;
D O I
10.1016/j.compmedimag.2018.05.002
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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.
引用
收藏
页码:16 / 24
页数:9
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