A deep learning-based approach to automatic proximal femur segmentation in quantitative CT images-Reference-Cited by-同舟云学术

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A deep learning-based approach to automatic proximal femur segmentation in quantitative CT images

Published:2022-03-24 Issue:5 Volume:60 Page:1417-1429
ISSN:0140-0118
Container-title:Medical & Biological Engineering & Computing
language:en
Short-container-title:Med Biol Eng Comput

Author:

Deng Yu,Wang Ling,Zhao Chen,Tang Shaojie,Cheng Xiaoguang,Deng Hong-Wen,Zhou Weihua

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shanxi Province

the Beijing Natural Science Foundation-Haidian Primitive Innovation Joint Fund

Beijing JST Research Funding

Xi'an Key Laboratory of Advanced Controlling and Intelligent Processing (ACIP), China

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11517-022-02529-9.pdf

Reference29 articles.

1. Johannesdottir F, Turmezei T, Poole KE (2014) Cortical bone assessed with clinical computed tomography at the proximal femur. J Bone Miner Res 29(4):771–783. https://doi.org/10.1002/jbmr.2199

2. Engelke K et al (2015) Clinical use of quantitative computed tomography (QCT) of the hip in the management of osteoporosis in adults: the 2015 ISCD official positions part I. J Clin Densitom 18(3):338–358. https://doi.org/10.1016/j.jocd.2015.06.012

3. Engelke K (2017) Quantitative computed tomography—current status and new developments. J Clin Densitom 20(3):309–321. https://doi.org/10.1016/j.jocd.2017.06.017

4. Black DM et al (2008) Proximal femoral structure and the prediction of hip fracture in men: a large prospective study using QCT. J Bone Miner Res 23(8):1326–1333. https://doi.org/10.1359/jbmr.080316

5. Manskeet SL et al (2009) Cortical and trabecular bone in the femoral neck both contribute to proximal femur failure load prediction. Osteoporos Int 20(3):445–453. https://doi.org/10.1007/s00198-008-0675-2

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