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Deniz CM, Xiang S, Hallyburton RS, Welbeck A, Babb JS, Honig S, Cho K, Chang G. Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks. Sci Rep 2018; 8:16485. [PMID: 30405145 PMCID: PMC6220200 DOI: 10.1038/s41598-018-34817-6] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Accepted: 10/26/2018] [Indexed: 11/20/2022] Open
Abstract
Magnetic resonance imaging (MRI) has been proposed as a complimentary method to measure bone quality and assess fracture risk. However, manual segmentation of MR images of bone is time-consuming, limiting the use of MRI measurements in the clinical practice. The purpose of this paper is to present an automatic proximal femur segmentation method that is based on deep convolutional neural networks (CNNs). This study had institutional review board approval and written informed consent was obtained from all subjects. A dataset of volumetric structural MR images of the proximal femur from 86 subjects were manually-segmented by an expert. We performed experiments by training two different CNN architectures with multiple number of initial feature maps, layers and dilation rates, and tested their segmentation performance against the gold standard of manual segmentations using four-fold cross-validation. Automatic segmentation of the proximal femur using CNNs achieved a high dice similarity score of 0.95 ± 0.02 with precision = 0.95 ± 0.02, and recall = 0.95 ± 0.03. The high segmentation accuracy provided by CNNs has the potential to help bring the use of structural MRI measurements of bone quality into clinical practice for management of osteoporosis.
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Affiliation(s)
- Cem M Deniz
- Department of Radiology, New York University School of Medicine, New York, NY, 10016, USA.
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, 10016, USA.
| | - Siyuan Xiang
- Center for Data Science, New York University, New York, NY, 10012, USA
| | | | - Arakua Welbeck
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, 10016, USA
| | - James S Babb
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, 10016, USA
| | - Stephen Honig
- Osteoporosis Center, Hospital for Joint Diseases, New York University Langone Medical Center, New York, NY, 10003, USA
| | - Kyunghyun Cho
- Center for Data Science, New York University, New York, NY, 10012, USA
- Courant Institute of Mathematical Science, New York University, New York, NY, 10012, USA
| | - Gregory Chang
- Department of Radiology, New York University School of Medicine, New York, NY, 10016, USA
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