Fast anatomy segmentation by combining low resolution multi-atlas label fusion with high resolution corrective learning: An experimental study

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 conduct an experimental study to investigate trade-off between computational cost and performance by first applying multi-atlas segmentation in coarse spatial resolution and then refining the results by learning-based error correction in the native image space. In a cardiac CT segmentation application, our experiments show that the new combination scheme can significantly reduce computational cost without losing accuracy.