A Deep Framework for Bone Age Assessment based on Finger Joint Localization
This work addresses the problem of high inter-observer and intra-observer variations in bone age assessment for diagnosing growth disorders in children, representing an incremental improvement over conventional methods.
The paper tackles bone age assessment by proposing a finger joint localization strategy to filter non-informative parts of hand images, which when combined with a full image-based deep network, results in improved performance compared to using whole hand images alone.
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.