Citations

How to Cite

There is not yet a journal paper describing this tool. Please cite the following abstract instead:

• Yushkevich, P.A., Pluta, J., Wang, H., Wisse, L.E., Das, S. and Wolk, D., 2016. Fast Automatic Segmentation of Hippocampal Subfields and Medial Temporal Lobe Subregions in 3 Tesla and 7 Tesla MRI. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 12(7), pp.P126-P127.
• Also please include the github link for greedy: https://github.com/pyushkevich/greedy

Relevant Publications

The paper below describes the greedy diffeomorphic approach very nicely:

• Joshi, S., Davis, B., Jomier, M. and Gerig, G., 2004. Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage, 23, pp.S151-S160.

The algorithms in greedy are closely related to and in many aspects derived from the SyN registration tool in ANTS. This tool, and particularly the metrics are described in:

• Avants, B.B., Epstein, C.L., Grossman, M. and Gee, J.C., 2008. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical image analysis, 12(1), pp.26-41.
• Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A. and Gee, J.C., 2011. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage, 54(3), pp.2033-2044.

The fast computation of the NCC metric is based on the sum-table algorithm originally described in:

• Tsai, D.M. and Lin, C.T., 2003. Fast normalized cross correlation for defect detection. Pattern Recognition Letters, 24(15), pp.2625-2631.

Fast smoothing of deformation fields is based on the ITK recursive Gaussian smoothing classes described in:

• Deriche, R., 1990. Fast algorithms for low-level vision. IEEE transactions on pattern analysis and machine intelligence, 12(1), pp.78-87.