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13 March 2024 Application of U-net segmentation in bone mineral density estimation via intelligent optical bone densitometry
Yun-Chien Hung, Chia-Wei Sun, Wei-Chun Chang, Yi-Min Wang, Gautam Takhellambam, Tsai-Hsueh Leu
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Proceedings Volume PC12832, Optics and Biophotonics in Low-Resource Settings X; PC128320B (2024) https://doi.org/10.1117/12.3000857
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
Early detection of osteoporosis is becoming imperative in an aging society, as low bone mineral density (BMD) poses an elevated risk of bone injury. We develop an optical bone densitometer (OBD) combined with deep learning to predict BMD in specific regions of human body. The OBD utilized a near-infrared light source to obtain photos with optical information from distal radius by emitting the light through the wrist, the relatively thin region of human body.To precisely capture the position of wrist, we employed U-net for biomedical image segmentation, which generates a mask for the original wrist image, leaving behind images without background noise for the subsequent deep learning analysis. The algorithm considers the preprocessed images and various physiological parameters to predict BMD in the target regions, thus providing a reliable result for both orthopedic surgeons and patients.
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Yun-Chien Hung, Chia-Wei Sun, Wei-Chun Chang, Yi-Min Wang, Gautam Takhellambam, and Tsai-Hsueh Leu "Application of U-net segmentation in bone mineral density estimation via intelligent optical bone densitometry", Proc. SPIE PC12832, Optics and Biophotonics in Low-Resource Settings X, PC128320B (13 March 2024); https://doi.org/10.1117/12.3000857
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KEYWORDS
Bone
Densitometry
Minerals
Image segmentation
Deep learning
Light sources
Osteoporosis
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