Automatic Localization of the Pons and Vermis on Fetal Brain MR Imaging Using a U-Net Deep Learning Model

作者：Farzan Vahedifard, Xuchu Liu, Jubril Adepoju, S Y Zhao, H Ai, Kranthi K. Marathu, Mark Supanich, Sharon E. Byrd, Jie Deng · 发表于：American Journal of Neuroradiology · 年份：2023 · DOI：10.3174/ajnr.a7978 · 被引用次数：7 · 研究领域：Fetal and Pediatric Neurological Disorders、Neonatal and fetal brain pathology、Prenatal Screening and Diagnostics

BACKGROUND AND PURPOSE: An MRI of the fetus can enhance the identification of perinatal developmental disorders, which improves the accuracy of ultrasound. Manual MRI measurements require training, time, and intra-variability concerns. Pediatric neuroradiologists are also in short supply. Our purpose was developing a deep learning model and pipeline for automatically identifying anatomic landmarks on the pons and vermis in fetal brain MR imaging and suggesting suitable images for measuring the pons and vermis. MATERIALS AND METHODS: We retrospectively used 55 pregnant patients who underwent fetal brain MR imaging with a HASTE protocol. Pediatric neuroradiologists selected them for landmark annotation on sagittal single-shot T2-weighted images, and the clinically reliable method was used as the criterion standard for the measurement of the pons and vermis. A U-Net-based deep learning model was developed to automatically identify fetal brain anatomic landmarks, including the 2 anterior-posterior landmarks of the pons and 2 anterior-posterior and 2 superior-inferior landmarks of the vermis. Four-fold cross-validation was performed to test the accuracy of the model using randomly divided and sorted gestational age-divided data sets. A confidence score of model prediction was generated for each testing case. RESULTS: Overall, 85% of the testing results showed a ≥90% confidence, with a mean error of <2.22 mm, providing overall better estimation results with fewer errors and higher ...