PT  - JOURNAL ARTICLE
AU  - Zhao, L.
AU  - Asis-Cruz, J.D.
AU  - Feng, X.
AU  - Wu, Y.
AU  - Kapse, K.
AU  - Largent, A.
AU  - Quistorff, J.
AU  - Lopez, C.
AU  - Wu, D.
AU  - Qing, K.
AU  - Meyer, C.
AU  - Limperopoulos, C.
TI  - Automated 3D Fetal Brain Segmentation Using an Optimized Deep Learning Approach
AID  - 10.3174/ajnr.A7419
DP  - 2022 Mar 01
TA  - American Journal of Neuroradiology
PG  - 448--454
VI  - 43
IP  - 3
4099  - http://www.ajnr.org/content/43/3/448.short
4100  - http://www.ajnr.org/content/43/3/448.full
SO  - Am. J. Neuroradiol.2022 Mar 01; 43
AB  - BACKGROUND AND PURPOSE: MR imaging provides critical information about fetal brain growth and development. Currently, morphologic analysis primarily relies on manual segmentation, which is time-intensive and has limited repeatability. This work aimed to develop a deep learning–based automatic fetal brain segmentation method that provides improved accuracy and robustness compared with atlas-based methods.MATERIALS AND METHODS: A total of 106 fetal MR imaging studies were acquired prospectively from fetuses between 23 and 39 weeks of gestation. We trained a deep learning model on the MR imaging scans of 65 healthy fetuses and compared its performance with a 4D atlas-based segmentation method using the Wilcoxon signed-rank test. The trained model was also evaluated on data from 41 fetuses diagnosed with congenital heart disease.RESULTS: The proposed method showed high consistency with the manual segmentation, with an average Dice score of 0.897. It also demonstrated significantly improved performance (P < .001) based on the Dice score and 95% Hausdorff distance in all brain regions compared with the atlas-based method. The performance of the proposed method was consistent across gestational ages. The segmentations of the brains of fetuses with high-risk congenital heart disease were also highly consistent with the manual segmentation, though the Dice score was 7% lower than that of healthy fetuses.CONCLUSIONS: The proposed deep learning method provides an efficient and reliable approach for fetal brain segmentation, which outperformed segmentation based on a 4D atlas and has been used in clinical and research settings.BSbrain stemCGMcortical GMCNNconvolutional neural networkCHDcongenital heart diseaseDGMdeep GMGAgestational age