Automatic segmentation of the fetal hippocampus using 3D deep convolution neural networks

Motivation: The ability to accurately segment the fetal hippocampus is critical to advancing our understanding of the origins of prenatal memory and emotional processing difficulties. Current manual methods are laborious and subjective. Goal(s): We aim to automate left and right fetal hippocampal segmentation in 3D MR images. Approach: We applied a 3D U-Net based model to automatically segment the left and right fetal hippocampus in 3D MR images. Results: Our dataset comprised 131 fetuses with 191 MRI scans. The results demonstrated high accuracy and efficiency, particularly for this challenging-to-segment structure, illuminating the potential of deep convolutional neural networks in this application. Impact: This study's automatic fetal hippocampal segmentation with deep learning has the potential to advance in utero brain development research and biomarker studies. The potential impact includes improving early diagnostics, in-utero neuro-surveillance, and future targeted therapeutics.