  Introduction: Fetal resting-state functional magnetic resonance imaging
(rs-fMRI) is a rapidly evolving field that provides valuable insight into brain
development before birth. Accurate segmentation of the fetal brain from the
surrounding tissue in nonstationary 3D brain volumes poses a significant
challenge in this domain. Current available tools have 0.15 accuracy. Aim: This
study introduced a novel application of artificial intelligence (AI) for
automated brain segmentation in fetal brain fMRI, magnetic resonance imaging
(fMRI). Open datasets were employed to train AI models, assess their
performance, and analyze their capabilities and limitations in addressing the
specific challenges associated with fetal brain fMRI segmentation. Method: We
utilized an open-source fetal functional MRI (fMRI) dataset consisting of 160
cases (reference: fetal-fMRI - OpenNeuro). An AI model for fMRI segmentation
was developed using a 5-fold cross-validation methodology. Three AI models were
employed: 3D UNet, VNet, and HighResNet. Optuna, an automated
hyperparameter-tuning tool, was used to optimize these models. Results and
Discussion: The Dice scores of the three AI models (VNet, UNet, and
HighRes-net) were compared, including a comparison between manually tuned and
automatically tuned models using Optuna. Our findings shed light on the
performance of different AI models for fetal resting-state fMRI brain
segmentation. Although the VNet model showed promise in this application,
further investigation is required to fully explore the potential and
limitations of each model, including the HighRes-net model. This study serves
as a foundation for further extensive research into the applications of AI in
fetal brain fMRI segmentation.
