Abstract
The purpose of the study was to develop a deep learning network for estimating and constructing highly accurate 3D bone models directly from actual X-ray images and to verify its accuracy. The data used were 173 computed tomography (CT) images and 105 actual X-ray images of a healthy wrist joint. To compensate for the small size of the dataset, digitally reconstructed radiography (DRR) images generated from CT were used as training data instead of actual X-ray images. The DRR-like images were generated from actual X-ray images in the test and adapted to the network, and high-accuracy estimation of a 3D bone model from a small data set was possible. The 3D shape of the radius and ulna were estimated from actual X-ray images with accuracies of 1.05 ± 0.36 and 1.45 ± 0.41 mm, respectively.
Highlights
The digitally reconstructed radiography (DRR) in the training dataset were generated by considering the line integral of the linear attenuation coefficient derived from the computed tomography (CT) value, resulting in the approximate range of [0.00–0.20] as shown for registration-DRR
This paper developed convolutional neural networks (CNNs) for estimating a 3D bone model solely based on 2D clinical X-ray images of living human bones
Extensive experiments using an actual clinical dataset were conducted to evaluate the applicability of the model in routine clinical practice
Summary
The purpose of the study was to develop a deep learning network for estimating and constructing highly accurate 3D bone models directly from actual X-ray images and to verify its accuracy
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