Bone age assessment is an important clinical trial to measure skeletal child maturity and diagnose of growth disorders. Conventional approaches such as the Tanner-Whitehouse (TW) and Greulich and Pyle (GP) may not perform well due to their large inter-observer and intra-observer variations. In this paper, we propose a finger joint localization strategy to filter out most non-informative parts of images. When combining with the conventional full image-based deep network, we observe a much-improved performance. % Our approach utilizes full hand and specific joints images for skeletal maturity prediction. In this study, we applied powerful deep neural network and explored a process in the forecast of skeletal bone age with the specifically combine joints images to increase the performance accuracy compared with the whole hand images.
@article{arxiv.1905.13124,
title = {A Deep Framework for Bone Age Assessment based on Finger Joint Localization},
author = {Xiaoman Zhang and Ziyuan Zhao and Cen Chen and Songyou Peng and Min Wu and Zhongyao Cheng and Singee Teo and Le Zhang and Zeng Zeng},
journal= {arXiv preprint arXiv:1905.13124},
year = {2019}
}