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

Towards A Device-Independent Deep Learning Approach for the Automated  Segmentation of Sonographic Fetal Brain Structures: A Multi-Center and  Multi-Device Validation