Semi-supervised Three-Dimensional Detection of Congenital Brain Anomalies in First Trimester Ultrasound

Congenital brain anomalies occur in about 1% of pregnancies and often lead to termination of pregnancy due to their severity. Detection rates in the first trimester are low, between 53% and 73%. Our goal is to improve the detection rate of anomaly screening through the development of an automated anomaly detection method. For our detection method, we developed a three-dimensional (3D) extension of EfficientAD, a state-of-the-art semi-supervised anomaly detection method originally designed for two-dimensional images. To assess the impact of insufficient image quality and preprocessing errors, we trained the method on both the original dataset and a subset that passed visual quality inspection. The original dataset consisted of 411 3D ultrasound images acquired in the 9^th week of pregnancy, with 404 images of 404 normally developing embryos and 7 images of 4 embryos with brain anomalies. The normal dataset was split in 70%/15%/15% for training, validation and testing, the anomalous dataset was only used for testing. We evaluated the models using the area under the receiver operating characteristic curve (AUC) and balanced accuracy. The method demonstrated good performance on the original dataset (AUC 0.87, accuracy 0.67). Model performance increased for the quality-checked subset (AUC 0.86, accuracy 0.83), indicating that excluding images with insufficient scan quality and preprocessing errors enhances overall model performance. In conclusion, our method lays the foundation for automatic anomaly detection in 3D ultrasound scans, ultimately leading to a higher detection rate of congenital anomalies in the first trimester.