Abstract: Fetal Cerebellar Hypoplasia(CH) is a severe developmental disorder of the central nervous system,the early diagnosis of which is crucial for the health of the foetus.This paper proposes a brain anatomy-based network(BAB-Net) for prenatal diagnosis of CH,aiming to improve the accuracy of ultrasound-based diagnosis.BAB-Net takes ultrasound images and brain anatomical features as inputs and uses an anatomy-constrained network for feature extraction and fusion.Ultrasound image data from a tertiary hospital between September 2019 and September 2023 are collected,including a total of 301 cases of CH-affected fetuses and 547 cases of normal fetuses.In these cases,the boundaries of the cerebellums,cisterna magnas,and skulls are marked by experienced sonographers.When the model training is completed,the classification accuracies of BAB-Net on two independent test sets reach 0.977 8 and 0.922 2 respectively,notebly superior to other mainstream networks.In cases where the gestational age is less than 30 weeks,BAB-Net showes higher accuracy.Further analysis finds that the influence of the anatomical structures of the fetal cerebellum and cisterna magna on the network performance is greater than that of the skull structure.By blended with the anatomy-constrained network,BAB-Net effectively improves the diagnostic accuracy of fetal CH,provides a new approach for prenatal screening of CH and offers important references for clinicians in pregnancy management and precise intervention.

Key words: Cerebellar Hypoplasia, Ultrasound, Medullary cistern, Anatomical structures, Deep learning