FetalGAN: Automated Segmentation of Fetal Functional Brain MRI Using Deep Generative Adversarial Learning and Multi-Scale 3D U-Net

Abstract

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 children and adults. 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 = 0.973 ± 0.013, precision = 0.977 ± 0.015; 3D U-Net, Dice score = 0.954 ± 0.054, precision = 0.967 ± 0.037; BET2, Dice score = 0.856 ± 0.084, precision = 0.758 ± 0.113. FetalGAN was also faster than 3D U-Net and the manual method (7.35 s vs. 10.25 s 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.

Advanced Machine Learning Approaches for Brain Mapping

“…Various methods (Isola et al, 2017;Han et al, 2018;Xue et al, 2018;Choi et al, 2019;Dong et al, 2019;Oh et al, 2020;Ding et al, 2021;Nishio et al, 2021;Wang T. et al, 2021;Zhan et al, 2021;Asis-Cruz et al, 2022) were proposed to explore the possibility of GAN in medical image segmentation. Xue et al (2018) used U-Net as the generator and proposed a multi-scale L 1 loss to minimize the distance of the feature maps of predictions and masks for the medical image segmentation of brain tumors.…”

A transformer-based generative adversarial network for brain tumor segmentation

“…Various methods (Isola et al, 2017 ; Han et al, 2018 ; Xue et al, 2018 ; Choi et al, 2019 ; Dong et al, 2019 ; Oh et al, 2020 ; Ding et al, 2021 ; He et al, 2021 ; Nishio et al, 2021 ; Wang T. et al, 2021 ; Zhan et al, 2021 ; Asis-Cruz et al, 2022 ) were proposed to explore the possibility of GAN in medical image segmentation. Xue et al ( 2018 ) used U-Net as the generator and proposed a multi-scale L 1 loss to minimize the distance of the feature maps of predictions and masks for the medical image segmentation of brain tumors.…”

Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics

“…Moreover, they used only binary class segmentation, while the proposed model addresses the problem of multi-tissue fetal brain segmentation. Asis et al [ 8 ] used an end-to-end generative adversarial neural network (GAN) to segment the fetal brain in functional magnetic resonance images (rs-fMRI). They segmented the full fetal brain and handled binary class problems by using a private dataset.…”