Manifold Ordinal-Mixup for Ordered Classes in TW3-Based Bone Age Assessment

Bone age assessment (BAA) is vital to detecting abnormal growth in children and can be used to investigate its cause. Automating assessments could benefit radiologists by reducing reader variability and reading time. Recently, deep learning (DL) algorithms have been devised to automate BAA using hand X-ray images mostly based on GP-based methods. In contrast to GP-based methods where radiologists compare the whole hand’s X-ray image with standard images in the GP-atlas, TW3 methods operate by analyzing major bones in the hand image to estimate the subject’s bone age. It is thus more attractive to automate TW3 methods for their lower reader variability and higher accuracy; however, the inaccessibility of bone maturity stages inhibited wide-spread application of DL in automating TW3 systems. In this work, we propose an unprecedented DL-based TW3 system by training deep neural networks (DNNs) to extract region of interest (RoI) patches in hand images for all 13 major bones and estimate the bone’s maturity stage which in turn can be used to estimate the bone age. For this purpose, we designed a novel loss function which considers ordinal relations among classes corresponding to maturity stages, and show that DNNs trained using our loss not only attains lower mean absolute error, but also learns a path-connected latent space illuminating the inherent ordinal relations among classes. Our experiments show that DNNs trained using the proposed loss outperform other DL algorithms, known to excel in other tasks, in estimating maturity stage and bone age.