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Automatic Detection of Osteochondral Lesions of the Talus via Deep Learning

Authors :
Gang Wang
Tiefeng Li
Lei Zhu
Siyuan Sun
Juntao Wang
Yidong Cui
Ben Liu
Yuliang Sun
Qingjia Xu
Jianmin Li
Source :
Frontiers in Physics, Vol 10 (2022)
Publication Year :
2022
Publisher :
Frontiers Media S.A., 2022.

Abstract

Screening of osteochondral lesions of the talus (OLTs) from MR imags usually requires time and efforts, and in most case lesions with small size are often missed in clinical practice. Thereore, it puts forward higher requirements for a more efficient OLTs screening method. To develop an automatic screening system for osteochondral lesions of the talus (OLTs), we collected 92 MRI images of patients with ankle pain from Qilu Hospital of Shandong University and proposed an AI (artificial intelligence) aided lesion screening system, which is automatic and based on deep learning method. A two-stage detection method based on the cascade R-CNN model was proposed to significantly improve the detection performance by taking advantage of multiple intersection-over-union thresholds. The backbone network was based on ResNet50, which was a state-of-art convolutional neural network model in image classification task. Multiple regression using cascaded detection heads was applied to further improve the detection precision. The mean average precision (mAP) that is adopted as major metrics in the paper and mean average recall (mAR) was selected to evaluate the performance of the model. Our proposed method has an average precision of 0.950, 0.975, and 0.550 for detecting the talus, gaps and lesions, respectively, and the mAP, mAR was 0.825, 0.930. Visualization of our network performance demonstrated the effectiveness of the model, which implied that accurate detection performance on these tasks could be further used in real clinical practice.

Details

Language :
English
ISSN :
2296424X
Volume :
10
Database :
Directory of Open Access Journals
Journal :
Frontiers in Physics
Publication Type :
Academic Journal
Accession number :
edsdoj.10c28e4696ae44728d812968043a8be6
Document Type :
article
Full Text :
https://doi.org/10.3389/fphy.2022.815560