Quantification of Pediatric Brain Development with X-ray CT Images using 3D-CNN

Abstract

Brain development in children between the ages of 0 and 3 is extremely rapid. In the diagnosis of the pediatric brain, quantitative methods for evaluating brain growth during this period are needed. We propose a method for predicting the developmental age of the brain using deep learning. In the proposed method, at first, the cranial region is extracted from CT images, and then the posture and position are calibrated. We propose a new neural network model that uses a 3D convolutional neural network (3D-CNN) to extract features from CT images and estimate the developmental age of the brain in all coupled layers. 204 pediatric patients (0 to 47 months) with no neurological abnormalities were used for study and evaluation. The root mean square error (RMSE) between the predicted age and the patient’s actual age was 6.45 months with a correlation coefficient of 0.89. In addition, the output of the attention map showed a high degree of attention to the anterior region. This result is consistent with medical findings that anterior regions of the pediatric brain are particularly developed.

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