Hierarchical Rough-to-Fine Model for Infant Age Prediction Based on Cortical Features

Abstract

Prediction of the chronological age based on neuroimaging data is important for brain development analysis and brain disease diagnosis. Although many researches have been conducted for age prediction of older children and adults, little work has been dedicated to infants. To this end, this paper focuses on predicting infant age from birth to 2-year old using brain MR images, as well as identifying some related biomarkers. However, brain development during infancy is too rapid and heterogeneous to be accurately modeled by the conventional regression models. To address this issue, a two-stage prediction method is proposed. Specifically, our method first roughly predicts the age range of an infant and then finely predicts the accurate chronological age based on a learned, age-group-specific regression model. Combining this two-stage prediction method with another complementary one-stage prediction method, a hierarchical rough-to-fine (HRtoF) model is built. HRtoF effectively splits the rapid and heterogeneous changes during a long time period into several short time ranges and further mines the discrimination capability of cortical features, thus reaching high accuracy in infant age prediction. Taking 8 types of cortical morphometric features from structural MRI as predictors, the effectiveness of our proposed HRtoF model is validated using an infant dataset including 50 healthy subjects with 251 longitudinal MRI scans from 14 to 797 days. Comparing with five state-of-the-art regression methods, HRtoF model reduces the mean absolute error of the prediction from >48 days to 32.1 days. The correlation coefficient of the predicted age and the chronological age reaches 0.963. Moreover, based on HRtoF, the relative contributions of the eight types of cortical features for age prediction are also studied.

Fig. 1.

The architecture of the proposed age prediction model: HRtoF.

Fig. 2.

Scatter plots of the chronological age and predicted age obtained by HRtoF, PLSR, SVR, GPR, ENLR, and NonS-GPR are shown in the 6 subfigures, respectively. The solid line describes the best predictions.

Fig. 3.

The relative importance of the 8 types of features varies from 1~3 months to 24 months. The relative importance of each feature type in each decision boundary was computed by summing the values of its related ROI features and normalized by dividing the total relative importance of the 8 feature types in the decision boundary.

Fig. 4.

The number of the ROIs related to each feature type (SDE, LGI, SDS, convexity, sharpness, thickness, area and volume).

Fig. 5.

The 58 selected most contributive features used in rough prediction stage are shown as red regions according to 8 feature types, i.e., SDE, LGI, SDS, convexity, sharpness, thickness, area and volume.

Fig. 6.

The development trajectories of the 8 types of global features from birth to 2 year of age. SDE, LGI, SDS, Convexity, Sharpness, and Thickness shown in this figure were obtained by averaging the corresponding values on all brain vertices; Area and Volume were obtained by summing the corresponding values on all brain vertices. Of note, these values were all normalized as z-scores for comparison.

Fig. 7.

The detailed information in the scatter plot of the chronological ages and predicted ages obtained by HRtoF. The circles with red crosses represent the prediction ages obtained by the ordinary prediction (Belief≤ θ). The circles without red crosses on it represent the prediction ages obtained by the two-stage prediction (Belief> θ). Red circles represent the corresponding scans, which were correctly classified by the rough prediction stage, while blue circles represent the corresponding scans that were incorrectly classified by the rough prediction stage.