Bone age assessment using general use convolutional neural networks Deep Learning methods have been applied to different medical imaging analysis tasks like, e.g., lesion classification and tissue segmentation. Bone age assessment is traditionally performed on an xray of the nondominant hand applying the Greulich and Pyle or the Tanner Whitehouse methods. In this work, we first have tested several stateoftheart Convolution Neural Networks models for assessing bone age that previously has shown great results in general computer vision tasks. Based on these results, we have developedoptimized a new model, which is presented here. For this purpose, we used transfer learning methods and trained the selected networks from scratch achieving a 7.89month error rate when assessing bone age in females.

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.