From the Department of Diagnostic Radiology and Nuclear Medicine (F.V., X.L., J.O.A., K.K.M., S.E.B.), Rush Medical College, Chicago, Illinois.
Department of Biostatistics (S.Z.), Yale School of Public Health, New Haven, Connecticut.
AJNR Am J Neuroradiol. 2023 Oct;44(10):1191-1200. doi: 10.3174/ajnr.A7978. Epub 2023 Aug 31.
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.
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.
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 confidence scores. The anterior and posterior pons and anterior vermis showed better estimation (which means fewer errors in landmark localization) and accuracy and a higher confidence level than other landmarks. We also developed a graphic user interface for clinical use.
This deep learning-facilitated pipeline practically shortens the time spent on selecting good-quality fetal brain images and performing anatomic measurements for radiologists.
胎儿磁共振成像（MRI）可增强围产期发育障碍的识别能力，从而提高超声检查的准确性。手动 MRI 测量需要培训、时间，且存在内在变异性问题。儿科神经放射科医生也很短缺。我们的目的是开发一种深度学习模型和流水线，用于自动识别胎儿脑 MRI 上脑桥和小脑蚓部的解剖标志，并为测量脑桥和小脑蚓部推荐合适的图像。
我们回顾性地使用 HASTE 协议对 55 例接受胎儿脑 MRI 的孕妇进行了研究。儿科神经放射科医生选择对矢状位单次激发 T2 加权图像进行标志标注，并使用临床可靠的方法作为脑桥和小脑蚓部测量的标准。开发了一个基于 U-Net 的深度学习模型，用于自动识别胎儿脑解剖标志，包括脑桥的 2 个前后标志和小脑蚓部的 2 个前后和 2 个上下标志。使用随机划分和排序的胎龄分组数据集进行四折交叉验证，以测试模型的准确性。为每个测试案例生成模型预测的置信度评分。
总体而言，85%的测试结果置信度≥90%，平均误差<2.22mm，提供了整体更好的估计结果，错误更少，置信度得分更高。前脑桥和后脑桥以及前小脑蚓部的估计（标志定位误差更小）和准确性以及置信度水平均优于其他标志。我们还开发了一个用于临床使用的图形用户界面。
这种深度学习辅助的流水线可显著缩短放射科医生选择高质量胎儿脑图像和进行解剖测量所花费的时间。
