Prediction of trabecular bone architectural features by deep learning models using simulated DXA images

Bone Reports
Pengwei XiaoXiaodu Wang

Abstract

Dual-energy X-ray absorptiometry (DXA) is widely used for clinical assessment of bone mineral density (BMD). Recent evidence shows that DXA images may also contain microstructural information of trabecular bones. However, no current image processing techniques could aptly extract the information. Inspired by the success of deep learning techniques in medical image analyses, we hypothesized in this study that DXA image-based deep learning models could predict the major microstructural features of trabecular bone with a reasonable accuracy. To test the hypothesis, 1249 trabecular cubes (6 mm × 6 mm × 6 mm) were digitally dissected out from the reconstruction of seven human cadaveric proximal femurs using microCT scans. From each cube, simulated DXA images in designated projections were generated, and the histomorphometric parameters (i.e., BV/TV, BS, Tb.Th, DA, Conn. D, and SMI) of the cube were determined using Image J. Convolutional neural network (CNN) models were trained using the simulated DXA images to predict the histomorphometric parameters of trabecular bone cubes. The results exhibited that the CNN models achieved high fidelity in predicting these histomorphometric parameters (from R = 0.80 to R = 0.985), showing that t...Continue Reading

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Citations

Jan 9, 2021·Osteoporosis International : a Journal Established As Result of Cooperation Between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA·D HansW D Leslie
Mar 23, 2021·Journal of Bone and Mineral Research : the Official Journal of the American Society for Bone and Mineral Research·Julien SmetsDidier Hans
Nov 7, 2021·Current Osteoporosis Reports·Valentina PedoiaSharmila Majumdar

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Methods Mentioned

BETA
X-ray
SMI

Software Mentioned

Keras
TensorFlow
ImageJ
SAS
ADAM
JMP PRO
MATLAB
BoneJ

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