Abstract
BACKGROUND: Early detection of central nervous system (CNS) anomalies in human embryos through prenatal screening is crucial for timely intervention and improved patient outcomes. Fetal brain mid-sagittal ultrasound images (FBMUIs) play a pivotal role as a diagnostic tool for detecting structural abnormalities. However, the automatic localization and quantitative segmentation of complex anatomical structures such as the corpus callosum-cavum septum pellucidum complex (CCC) and cerebellar vermis (CV) in FBMUIs present significant challenges. METHODS: To address this issue, we propose an integrated framework that combines anatomical knowledge with computer vision techniques. Our framework comprises four steps: (I) generation of average templates for CCC and CV local images using a variational autoencoder (VAE); (II) localizing the CCC by using the "Initial Localization-Accurate Localization-Result Detection" strategy, followed by segmenting it based on morphological characteristics using the "Initial Contour Fitting-Contour Iteration" strategy; (III) applying a similar strategy as CCC localization and CV segmentation; and (IV) leveraging spatial and morphological characteristics to achieve accurate localization and segmentation. RESULTS: Our CCC and CV localization and segmentation methods were validated by using 140 FBMUIs from various perspectives. The accuracy and effectiveness of our approach were demonstrated through data statistics and comparative analysis. Currently, clinical trials are being conducted on our method at Shengjing Hospital of China Medical University. CONCLUSIONS: Our proposed integrated framework presents a novel solution for the automatic localization and quantitative segmentation of the CCC and CV in FBMUIs. It shows promise for early diagnosis of CNS anomalies in human embryos, offering significant clinical implications.