  Tract-specific diffusion measures, as derived from brain diffusion MRI, have
been linked to white matter tract structural integrity and neurodegeneration.
As a consequence, there is a large interest in the automatic segmentation of
white matter tract in diffusion tensor MRI data. Methods based on the
tractography are popular for white matter tract segmentation. However, because
of the limited consistency and long processing time, such methods may not be
suitable for clinical practice. We therefore developed a novel convolutional
neural network based method to directly segment white matter tract trained on a
low-resolution dataset of 9149 DTI images. The method is optimized on input,
loss function and network architecture selections. We evaluated both
segmentation accuracy and reproducibility, and reproducibility of determining
tract-specific diffusion measures. The reproducibility of the method is higher
than that of the reference standard and the determined diffusion measures are
consistent. Therefore, we expect our method to be applicable in clinical
practice and in longitudinal analysis of white matter microstructure.
