Artificial Intelligence in Fetal Resting-State Functional MRI Brain Segmentation: A Comparative Analysis of 3D UNet, VNet, and HighRes-Net Models

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

Fetal MRI Analysis of Corpus Callosal Abnormalities: Classification, and Associated Anomalies

Background. Corpus callosal abnormalities (CCA) are midline developmental brain malformations and are usually associated with a wide spectrum of other neurological and non-neurological abnormalities. The study aims to highlight the diagnostic role of fetal MRI to characterize heterogeneous corpus callosal abnormalities using the latest classification system. It also helps to identify associated anomalies, which have prognostic implications for the postnatal outcome. Methods. In this study, retrospective data from antenatal women who underwent fetal MRI between January 2014 and July 2023 at Rush University Medical Center were evaluated for CCA and classified based on structural morphology. Patients were further assessed for associated neurological and non-neurological anomalies. Results. The most frequent class of CCA was complete agenesis (79.1%), followed by hypoplasia (12.5%), dysplasia (4.2%), and hypoplasia with dysplasia (4.2%). Among them, 17% had isolated CCA, while the majority (83%) had complex forms of CCA associated with other CNS and non-CNS anomalies. Out of the complex CCA cases, 58% were associated with other CNS anomalies, while 8% were associated with non-CNS anomalies. 17% of cases had both. Conclusion. The use of fetal MRI is valuable in the classification of abnormalities of the corpus callosum after the confirmation of a suspected diagnosis on prenatal ultrasound. This technique is an invaluable method for distinguishing between isolated and complex forms of CCA, especially in cases of apparent isolated CCA. The use of diffusion-weighted imaging or diffusion tensor imaging in fetal neuroimaging is expected to provide further insights into white matter abnormalities in fetuses diagnosed with CCA in the future