Classification of Neurodevelopmental Age in Normal Infants Using 3DCNN based on Brain MRI Human brain development is rapid during infancy and early childhood. Many disease processes impair this development. Therefore, brain developmental age estimation (BDAE) is essential for all diseases affecting cognitive development. Brain magnetic resonance imaging (MRI) of infants shows brain growth and morphologic patterns during childhood. Therefore, we can estimate the developmental age from brain images. However, MRI analysis is time consuming because each scan contains millions of data points (voxels). We investigated the threedimensional convolutional neural network (3D CNN), a deep learning algorithm, to rapidly classify neurodevelopmental age with high accuracy based on MRIs. MRIs from normal newborns were obtained from the National Institute of Mental Health (NIMH) Data Archive. Age categories of pediatric MRIs were 3 wks xc2xb1 1 wk, 1 yr xc2xb1 2 wks, and 3 yrs xc2xb1 4 wks. We trained a BDAE method using T1, T2, and proton density (PD) images from MRI scans of 112 individuals using 3D CNN. Compared with the known age, our method has a sensitivity of 99% and specificity of 98.3%. Moreover, our 3D CNN model has better performance in neurodevelopmental age estimation than does 2D CNN.

Bone Age Assessment by Deep Convolutional Neural Networks Combined with Clinical TW3RUS Bone age assessment is critical to diagnosis of various growth disorders in children, such as endocrine, nutritional disorders and dysplasia. Xrays of hand and wrist are the most common modality used to calculate bone age. In this paper, we propose a novel approach called DeepTW3 for automatic bone age assessment from Xray images. DeepTW3 integrates Convolutional Neural Networks (CNNs) with expertise knowledge of TW3(TannerWhitehouse 3nd edition)RUS(radius, ulna and short bones) bone age assessment system. The proposed method is tested on a dataset containing 1,100 hand bone Xray images, all of which were manually annotated with selected region of interests(ROIs). Our method achieved mean absolute errors (MAE) of 0.2685, outperforming all stateoftheart methods. For the task of grading skeletal maturity, our method using continuous stage distribution is complementary to using the clinical TW3RUS categorical stages when interpreting critical cases of intermediate bone stage.