Neuro4Neuro

Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging

Li, Bo (Northeastern University China; Erasmus MC)
de Groot, Marius (Erasmus MC)
Steketee, Rebecca M.E. (Erasmus MC)
Meijboom, Rozanna (Erasmus MC; University of Edinburgh)
Smits, Marion (Erasmus MC)
Vernooij, Meike W. (Erasmus MC)
Ikram, M. Arfan (Erasmus MC)
Liu, Jiren (Northeastern University China)
Niessen, W.J. (TU Delft ImPhys/Medical Imaging; TU Delft ImPhys/Computational Imaging; Erasmus MC)

2020

Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized from brain diffusion MRI. In addition, to enable analysis of WM tracts in large datasets and in clinical practice it is essential to have methodology that is fast and easy to apply. This work therefore presents a new approach for WM tract segmentation: Neuro4Neuro, that is capable of direct extraction of WM tracts from diffusion tensor images using convolutional neural network (CNN). This 3D end-to-end method is trained to segment 25 WM tracts in aging individuals from a large population-based study (N ​= ​9752, 1.5T MRI). The proposed method showed good segmentation performance and high reproducibility, i.e., a high spatial agreement (Cohen's kappa, κ=0.72−0.83) and a low scan-rescan error in tract-specific diffusion measures (e.g., fractional anisotropy: ε=1%−5%). The reproducibility of the proposed method was higher than that of a tractography-based segmentation algorithm, while being orders of magnitude faster (0.5s to segment one tract). In addition, we showed that the method successfully generalizes to diffusion scans from an external dementia dataset (N ​= ​58, 3T MRI). In two proof-of-principle experiments, we associated WM microstructure obtained using the proposed method with age in a normal elderly population, and with disease subtypes in a dementia cohort. In concordance with the literature, results showed a widespread reduction of microstructural organization with aging and substantial group-wise microstructure differences between dementia subtypes. In conclusion, we presented a highly reproducible and fast method for WM tract segmentation that has the potential of being used in large-scale studies and clinical practice.

CNN
Diffusion MRI
Neurodegeneration
Segmentation
White matter tract

http://resolver.tudelft.nl/uuid:2775b392-3423-4360-9ad9-ca130f550de6

https://doi.org/10.1016/j.neuroimage.2020.116993

1053-8119

NeuroImage, 218

Institutional Repository

journal article

© 2020 Bo Li, Marius de Groot, Rebecca M.E. Steketee, Rozanna Meijboom, Marion Smits, Meike W. Vernooij, M. Arfan Ikram, Jiren Liu, W.J. Niessen, More Authors