  The fetal cortical plate (CP) undergoes drastic morphological changes during
the in utero development. Therefore, CP growth and folding patterns are key
indicator in the assessment of the brain development and maturation. Magnetic
resonance imaging (MRI) offers specific insights for the analysis of
quantitative imaging biomarkers. Nonetheless, accurate and, more importantly,
topologically correct MR image segmentation remains the key baseline to such
analysis. In this study, we propose a deep learning segmentation framework for
automatic and morphologically consistent segmentation of the CP in fetal brain
MRI. Our contribution is two fold. First, we generalized a multi-dimensional
topological loss function in order to enhance the topological accuracy. Second,
we introduced hole ratio, a new topology-based validation measure that
quantifies the size of the topological defects taking into account the size of
the structure of interest. Using two publicly available datasets, we
quantitatively evaluated our proposed method based on three complementary
metrics which are overlap-, distance- and topology-based on 27 fetal brains.
Our results evidence that our topology-integrative framework outperforms
state-of-the-art training loss functions on super-resolution reconstructed
clinical MRI, not only in shape correctness but also in the classical
evaluation metrics. Furthermore, results on additional 31 out-of-domain SR
reconstructions from clinical acquisitions were qualitatively assessed by three
experts. The experts' consensus ranked our TopoCP method as the best
segmentation in 100\% of the cases with a high inter-expert agreement. Overall,
both quantitative and qualitative results, on a wide range of gestational ages
and number of cases, support the generalizability and added value of our
topology-guided framework for fetal CP segmentation.
