Authors: Jon Pipitone, Jason P. Lerch, Miriam Friedel, Aristotle N. Voineskos, M. Mallar Chakravarty
Abstract:
Neuroimaging research often relies on automated anatomical segmentations of MR images of the brain. Current multi-atlas based approaches provide accurate segmentations of brain images by propagating manually derived segmentations of specific neuroanatomical structures to unlabelled data. These approaches often rely on a large number of such manually segmented atlases that take significant time and expertise to produce. We present an algorithm for the automatic segmentation of the hippocampus that minimizes the number of atlases needed while still achieving similar accuracy to multi-atlas approaches. We perform repeated random subsampling validation on the International Brain Segmentation Repository (IBSR) dataset to compare our approach to basic multi-atlas segmentation using the full IBSR dataset, and to single-atlas (model-based) segmentation. Our results show that with only 8 input atlases, MAGeT brain can achieve to within 2.0% segmentation accuracy of the basic multi-atlas approach using 17 input atlases (mean κ = 0.775 vs. κ = 0.791). These results compare favourably to those of other investigators who have used the IBSR data for validation.
This paper was submitted to ISBR 2012, but was not accepted.
To build this paper, you will need a LaTeX distribution, R, and the R knitr and ggplot2 packages. On Ubuntu 14.04, this will get you started:
$ sudo apt-get install texlive-latex-base texlive-latex-extra \
texlive-fonts-extra texlive-fonts-recommended r-core r-cran-plyr \
r-cran-ggplot2
$ echo "install.packages('knitr')" | R
$ make clean && make