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A prior knowledge-guided convolutional neural network model for predicting fetal brain age from MRI during second and
Fangmei Zhu1, Shijie Huang2, Xue Tang3
1Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, No.261, Huansha Road, Hangzhou, Zhejiang, 310006, China.
Background:
This study aimed to evaluate the efficacy of a convolutional neural network (CNN) model in estimating fetal brain age from MRI scans during second and third trimesters.
Methods:
A total of 407 T2-weighted MRI including 2310 stacks in the axial, sagittal, and coronal planes were used. The average fetal brain age was 30.7 weeks (ranging from 22 to 39 weeks). The reference standard gestational age was based on ultrasonography measurements during the first trimester. A prior knowledge-guided CNN model was utilized for brain age prediction. The relationship between the predicted brain age and the reference standard was assessed using Lin's concordance correlation coefficient (ρc) and determination coefficient (R2) score. The results of fetal brain age prediction were presented as mean absolute error (MAE). We combined the features extracted by the brain age prediction network, that is attention maps, with the original images as input for the brain extraction network, in order to further assess whether these features were highly correlated with brain regions. An independent t-test was used to compare our method with different age prediction and brain extraction methods reported in the existing literatures.
Results:
The model exhibited a MAE of 4.62 ± 3.31 days, aligning closely with the reference standard (ρc = 0.977) and demonstrating a robust correlation between predicted and actual brain age (R2 = 0.953). Our model achieved the lowest MAE and the highest R2, showing statistically significant difference compared to other methods (all P-values<0.001). Our proposed method, which combined the U-Net model with an input of images and attention maps, exhibited significantly superior segmentation performance compared to existing methods (all P-values < 0.001). The attention map demonstrated that our prediction model was capable of extracting features that are highly correlated with fetal central brain regions.
Conclusion:
A prior knowledge-guided CNN model that utilized fetal brain MRI data from the second and third trimesters can accurately predict fetal brain age, which holds significant potential for optimizing pregnancy management.
