Automated 3D Fetal Brain Segmentation Using an Optimized Deep Learning Approach

Abstract

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.ABBREVIATIONS: BS ¼ brain stem; CGM ¼ cortical GM; CNN ¼ convolutional neural network; CHD ¼ congenital heart disease; DGM ¼ deep GM; GA ¼ gestational age I n vivo fetal brain MR imaging has provided critical insight into normal fetal brain development and has led to improved and more accurate diagnoses of brain abnormalities in the high-risk fetus. 1 Morphologic fetal MR imaging studies have been used to quantify disturbances in fetal brain development associated with congenital heart disease (CHD). 2 However, image segmentation, an essential step in morphologic analysis, is time-consuming and prone to inter-/intraobserver variability.There are 3 major challenges in fetal MR imaging that affect image quality and reliable anatomic delineation. First, fetal brain anatomy changes rapidly with advancing gestational age (GA), resulting in dramatic morphologic changes in brain tissues. Cortical maturation (ie, gyrification and sulcation) during the second and third trimesters transforms the smooth fetal surface into a highly convoluted structure. Second, changes in water content accompanying active myelination introduce high variations in MR imaging signal intensity and contrast across GAs. 3,4 Third, at times, artifacts corrupt fetal images. For example, maternal respiration and irregular fetal movements often result in motion artifacts. Differences in conductivity between amniotic fluid and tissues can ca...

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