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arXiv · 1905.13124

A Deep Framework for Bone Age Assessment based on Finger Joint Localization

Xiaoman Zhang·Ziyuan Zhao·Cen Chen·Songyou Peng·Min Wu·Zhongyao Cheng·Singee Teo·Le Zhang·Zeng Zeng
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Abstract

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.

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Xiaoman Zhang, Ziyuan Zhao, Cen Chen, Songyou Peng, Min Wu, Zhongyao Cheng, Singee Teo, Le Zhang, Zeng Zeng. 2019-05-07. A Deep Framework for Bone Age Assessment based on Finger Joint Localization. https://arxiv.org/abs/1905.13124

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