Title:An Automatic Classification of the Early Osteonecrosis of Femoral Head with Deep Learning
VOLUME: 16 ISSUE: 10
Author(s):Liyang Zhu, Jungang Han, Renwen Guo, Dong Wu, Qiang Wei, Wei Chai and Shaojie Tang*
Affiliation:School of Computer Science, Xi’an University of Posts & Telecommunications, Xi’an 710121, School of Computer Science, Xi’an University of Posts & Telecommunications, Xi’an 710121, Department of Orthopaedics, Chinese PLA General Hospital, Beijing 100039, Department of Orthopaedics, Chinese PLA General Hospital, Beijing 100039, School of Computer Science, Xi’an University of Posts & Telecommunications, Xi’an 710121, Department of Orthopaedics, Chinese PLA General Hospital, Beijing 100039, School of Automation, Xi’an University of Posts & Telecommunications, Xi’an 710121
Keywords:Osteonecrosis of femoral head, convolutional neural network, conditional generative adversarial network, convolutional
autoencoder, K-means clustering, peak signal-to-noise ratios.
Abstract:
Background: Osteonecrosis of Femoral Head (ONFH) is a common complication in
orthopaedics, wherein femoral structures are usually damaged due to the impairment or interruption
of femoral head blood supply.
Aim: In this study, an automatic approach for the classification of the early ONFH with deep
learning has been proposed.
Methods: All femoral CT slices according to their spatial locations with the Convolutional Neural
Network (CNN) are first classified. Therefore, all CT slices are divided into upper, middle or lower
segments of femur head. Then the femur head areas can be segmented with the Conditional
Generative Adversarial Network (CGAN) for each part. The Convolutional Autoencoder is employed
to reduce dimensions and extract features of femur head, and finally K-means clustering is
used for an unsupervised classification of the early ONFH.
Results: To invalidate the effectiveness of the proposed approach, the experiments on the dataset
with 120 patients are carried out. The experimental results show that the segmentation accuracy is
higher than 95%. The Convolutional Autoencoder can reduce the dimension of data, the Peak Signal-
to-Noise Ratios (PSNRs) are better than 34dB for inputs and outputs. Meanwhile, there is a
great intra-category similarity, and a significant inter-category difference.
Conclusion: The research on the classification of the early ONFH has a valuable clinical merit,
and hopefully it can assist physicians to apply more individualized treatment for patient.
