  The fetal cortical plate undergoes drastic morphological changes throughout
early in utero development that can be observed using magnetic resonance (MR)
imaging. An accurate MR image segmentation, and more importantly a
topologically correct delineation of the cortical gray matter, is a key
baseline to perform further quantitative analysis of brain development. In this
paper, we propose for the first time the integration of a topological
constraint, as an additional loss function, to enhance the morphological
consistency of a deep learning-based segmentation of the fetal cortical plate.
We quantitatively evaluate our method on 18 fetal brain atlases ranging from 21
to 38 weeks of gestation, showing the significant benefits of our method
through all gestational ages as compared to a baseline method. Furthermore,
qualitative evaluation by three different experts on 130 randomly selected
slices from 26 clinical MRIs evidences the out-performance of our method
independently of the MR reconstruction quality.
