  Purpose: The purpose is to design a novelty automatic diagnostic method for
osteoporosis screening by using the potential capability of convolutional
neural network (CNN) in feature representation and extraction, which can be
incorporated into the procedure of routine CT diagnostic in physical
examination thereby improving the osteoporosis diagnosis and reducing the
patient burden. Methods: The proposed convolutional neural network-based method
mainly comprises two functional modules to perform automatic detection of bone
condition by analyzing the diagnostic CT image. The first functional module
aims to locate and segment the ROI of diagnostic CT image, called
Mark-Segmentation-Network (MS-Net). The second functional module is used to
determine the category of bone condition by the features of ROI, called
Bone-Conditions-Classification-Network (BCC-Net). The trained MS-Net can get
the mark image of inputted original CT image, thereby obtain the segmentation
image. The trained BCC-Net can obtain the probability value of normal bone
mass, low bone mass, and osteoporosis by inputting the segmentation image. On
the basis of network results, the radiologists can provide preliminary
qualitative diagnosis results of bone condition. Results: The proposed MS-Net
has an excellent segmentation precision on the shape preservation of different
lumbar vertebra. The numerical indicators of segmentation results are greater
than 0.998. The proposed BCC-Net was evaluated with 3,024 clinical images and
achieved an accuracy of 76.65% and an area under the receiver operating
characteristic curve of 0.9167. Conclusions: This study proposed a novel
two-step method for automatic detection of bone conditions via diagnostic CT
images and it has great potential in clinical applications for osteoporosis
screening. The method can potentially reduce the manual burden to radiologists
and diagnostic cost to patients.
