Year: 2019
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Zhang, Xiaoman, Zhao, Ziyuan, Chen, Cen, Peng, Songyou, Wu, Min, Cheng, Zhongyao, Teo, Singee, Zhang, Le, Zeng, Zeng
Language: English
Subfield: Archeology
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2947177139