  Diffusion-weighted Magnetic Resonance Imaging (dMRI) is increasingly used to
study the fetal brain in utero. An important computation enabled by dMRI is
streamline tractography, which has unique applications such as tract-specific
analysis of the brain white matter and structural connectivity assessment.
However, due to the low fetal dMRI data quality and the challenging nature of
tractography, existing methods tend to produce highly inaccurate results. They
generate many false streamlines while failing to reconstruct streamlines that
constitute the major white matter tracts. In this paper, we advocate for
anatomically constrained tractography based on an accurate segmentation of the
fetal brain tissue directly in the dMRI space. We develop a deep learning
method to compute the segmentation automatically. Experiments on independent
test data show that this method can accurately segment the fetal brain tissue
and drastically improve tractography results. It enables the reconstruction of
highly curved tracts such as optic radiations. Importantly, our method infers
the tissue segmentation and streamline propagation direction from a diffusion
tensor fit to the dMRI data, making it applicable to routine fetal dMRI scans.
The proposed method can lead to significant improvements in the accuracy and
reproducibility of quantitative assessment of the fetal brain with dMRI.
