Abstract
Neighbourhood tractography aims to automatically segment equivalent brain white matter tracts from diffusion magnetic resonance imaging (dMRI) data in different subjects by using a “reference tract” as a prior for the shape and length of each tract of interest. In the current work we present a means of improving the technique by using references tracts derived from dMRI data acquired from 80 healthy volunteers aged 25–64 years. The reference tracts were tested on the segmentation of 16 major white matter tracts in 50 healthy older people, aged 71.8 (±0.4) years. We found that data-generated reference tracts improved the automatic white matter tract segmentations compared to results from atlas-generated reference tracts. We also obtained higher percentages of visually acceptable segmented tracts and lower variation in water diffusion parameters using this approach.
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Acknowledgements
LBC1936 was supported by the Age UK-funded Disconnected Mind project, with additional funding from the UK Medical Research Council (MR/M013111/1). MRI scanning for the training dataset was funded under NIH grant R01 EB004155-03. The scanning was performed at the Brain Research Imaging Centre, Edinburgh, part of Edinburgh Imaging (www.ed.ac.uk/clinical-sciences/edinburgh-imaging) and the SINAPSE Collaboration (Scottish Imaging Network, A Platform for Scientific Excellence, www.sinapse.ac.uk).
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Muñoz Maniega, S., Bastin, M.E., Deary, I.J., Wardlaw, J.M., Clayden, J.D. (2017). Improved Reference Tracts for Unsupervised Brain White Matter Tractography. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_37
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