Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints

Xiaobin Hu,Yuhong Liu,Jian Song,Jian Gao,Zhenhua Liao,Bjoern Menze,Weiqiang Liu

doi:10.1007/s40544-021-0516-2

Abstract

Finding the correct category of wear particles is important to understand the tribological behavior. However, manual identification is tedious and time-consuming. We here propose an automatic morphological residual convolutional neural network (M-RCNN), exploiting the residual knowledge and morphological priors between various particle types. We also employ data augmentation to prevent performance deterioration caused by the extremely imbalanced problem of class distribution. Experimental results indicate that our morphological priors are distinguishable and beneficial to largely boosting overall performance. M-RCNN demonstrates a much higher accuracy (0.940) than the deep residual network (0.845) and support vector machine (0.821). This work provides an effective solution for automatically identifying wear particles and can be a powerful tool to further analyze the failure mechanisms of artificial joints.

Highlights

The coronavirus disease (COVID-19) pandemic in 2020 has made people care more about health problems
Based on the wear particles we obtained in our previous work [13] and motivated by the considerations above, in this paper, we propose a morphological residual convolutional neural network (M-RCNN) as an automatic wear particle classification model to tackle the extremely class imbalance problem
We analyze our morphological residual convolutional neural network (M-RCNN) on the 181 unseen test data to show our superiority compared with the conventional networks, i.e., support vector machine (SVM), deep residual network (ResNet), and deep residual network with data augmentation (ResNet+Aug) models

Summary

Introduction

The coronavirus disease (COVID-19) pandemic in 2020 has made people care more about health problems. These epidemic diseases and chronic diseases should be brought to the forefront. After long-term service in the human body, the articular surfaces will be worn and generate wear particles. It has been reported that the wear particles of artificial joint prosthesis may induce host response in the human body and cause a so-called “particle disease” [7]. Wear particles in the size of micrometers or less can lower bone density through osteolysis and further lead to aseptic loosening [8], making particle related disease one of the top leading causes for prosthesis failure after long-term implantation [9]. It is crucial to carry out failure analysis through identifying wear particles whose shape and surface morphologies are closely related to their generation mechanisms and wear conditions

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Discussion

Conclusion