Abstract
We are concerned with the segmentation of structures within the brain particularly the gyri of the cerebral cortex, but also subcortical structures from volumetric T1-weighted MRI images. A fully automatic multi-atlas registration based segmentation approach is used to label novel data. We use a standard affine registration method combined with a small deformation (non-diffeomorphic), non-linear registration method which optimises mutual information, with a cascading set of regularisation parameters. We consistently segment 138 structures in the brain, 98 in the cortex and 40 in the sub-cortex. An overall Dice score of 0.704 and a mean surface distance of 1.106 mm is achieved in leave-one-out cross validation using all available atlases. The algorithm has been evaluated on a number of different cohorts which includes patients of different ages and scanner manufacturers, and has been shown to be robust. It is shown to have comparable accuracy to other state of the art methods using the MICCAI 2012 multi-atlas challenge benchmark, but the runtime is substantially less.
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Murphy, S., Mohr, B., Fushimi, Y., Yamagata, H., Poole, I. (2014). Fast, Simple, Accurate Multi-atlas Segmentation of the Brain. In: Ourselin, S., Modat, M. (eds) Biomedical Image Registration. WBIR 2014. Lecture Notes in Computer Science, vol 8545. Springer, Cham. https://doi.org/10.1007/978-3-319-08554-8_1
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DOI: https://doi.org/10.1007/978-3-319-08554-8_1
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