FetalGAN: Automated Segmentation of Fetal Functional Brain MRI Using Deep Generative Adversarial Learning and Multi-Scale 3D U-Net - De Asis-Cruz, Josepheen; Krishnamurthy, Dhineshvikram; Jose, Chris; Cook, Kevin M.; Limperopoulos, Catherine (Frontiers in Neuroscience, 2022)
An important step in the preprocessing of resting state functional magnetic resonance images (rs-fMRI) is the separation of brain from non-brain voxels. Widely used imaging tools such as FSL’s BET2 and AFNI’s 3dSkullStrip accomplish this task effectively. In fetal functional brain imaging, however, the presence of maternal tissue around the brain coupled with the non-standard position of the fetal head limit the usefulness of these tools. Accurate brain masks are thus generated manually, a time-consuming and tedious process that slows down preprocessing of fetal rs-fMRI. Recently, deep learning-based segmentation models such as convolutional neural networks (CNNs) have been increasingly used for automated segmentation of medical images, including the fetal brain. Here, we propose a computationally efficient end-to-end generative adversarial neural network (GAN) for segmenting the fetal brain. This method, which we call FetalGAN, yielded whole brain masks that closely approximated the manually labeled ground truth. FetalGAN performed better than 3D U-Net model and BET2: FetalGAN, dice score=0.942+/-0.095, precision=0.973+/-0.040; 3D U-Net, dice score=0.930+/-0.098, precision = 0.940+/-0.102, BET2, dice score=0.841+/-0.101, precision=0.747+/-0.126. FetalGAN was also faster than 3D U-Net and the manual method (7.35 sec vs 10.25 sec vs ~5 min/volume). To the best of our knowledge, this is the first successful implementation of 3D CNN with GAN on fetal fMRI brain images and represents a significant advance in fully automating processing of rs-MRI images.