  Fetal cortical plate segmentation is essential in quantitative analysis of
fetal brain maturation and cortical folding. Manual segmentation of the
cortical plate, or manual refinement of automatic segmentations is tedious and
time-consuming. Automatic segmentation of the cortical plate, on the other
hand, is challenged by the relatively low resolution of the reconstructed fetal
brain MRI scans compared to the thin structure of the cortical plate, partial
voluming, and the wide range of variations in the morphology of the cortical
plate as the brain matures during gestation. To reduce the burden of manual
refinement of segmentations, we have developed a new and powerful deep learning
segmentation method. Our method exploits new deep attentive modules with mixed
kernel convolutions within a fully convolutional neural network architecture
that utilizes deep supervision and residual connections. We evaluated our
method quantitatively based on several performance measures and expert
evaluations. Results show that our method outperforms several state-of-the-art
deep models for segmentation, as well as a state-of-the-art multi-atlas
segmentation technique. We achieved average Dice similarity coefficient of
0.87, average Hausdorff distance of 0.96 mm, and average symmetric surface
difference of 0.28 mm on reconstructed fetal brain MRI scans of fetuses scanned
in the gestational age range of 16 to 39 weeks. With a computation time of less
than 1 minute per fetal brain, our method can facilitate and accelerate
large-scale studies on normal and altered fetal brain cortical maturation and
folding.
