  Quality assessment of prenatal ultrasonography is essential for the screening
of fetal central nervous system (CNS) anomalies. The interpretation of fetal
brain structures is highly subjective, expertise-driven, and requires years of
training experience, limiting quality prenatal care for all pregnant mothers.
With recent advancement in Artificial Intelligence (AI), specifically deep
learning (DL), assistance in precise anatomy identification through semantic
segmentation essential for the reliable assessment of growth and
neurodevelopment, and detection of structural abnormalities have been proposed.
However, existing works only identify certain structures (e.g., cavum septum
pellucidum, lateral ventricles, cerebellum) from either of the axial views
(transventricular, transcerebellar), limiting the scope for a thorough
anatomical assessment as per practice guidelines necessary for the screening of
CNS anomalies. Further, existing works do not analyze the generalizability of
these DL algorithms across images from multiple ultrasound devices and centers,
thus, limiting their real-world clinical impact. In this study, we propose a DL
based segmentation framework for the automated segmentation of 10 key fetal
brain structures from 2 axial planes from fetal brain USG images (2D). We
developed a custom U-Net variant that uses inceptionv4 block as a feature
extractor and leverages custom domain-specific data augmentation.
Quantitatively, the mean (10 structures; test sets 1/2/3/4) Dice-coefficients
were: 0.827, 0.802, 0.731, 0.783. Irrespective of the USG device/center, the DL
segmentations were qualitatively comparable to their manual segmentations. The
proposed DL system offered a promising and generalizable performance
(multi-centers, multi-device) and also presents evidence in support of
device-induced variation in image quality (a challenge to generalizibility) by
using UMAP analysis.
