Detection Method of Athlete Joint Injury Based on Deep Learning Model

被引：0

作者：

Liu J. ^{[1
,2
]}

Yang X. ^{[1
]}

Liao T. ^{[1
]}

Huang Y. ^{[1
]}

机构：

[1] Department of Orthopedics, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu

[2] School of Public Affairs and Law, Southwest Jiaotong University, Chengdu

来源：

Computational and Mathematical Methods in Medicine | 2022年 / 2022卷

关键词：

Compendex;

D O I：

10.1155/2022/8165580

中图分类号：

学科分类号：

摘要：

The research on accurate and intelligent segmentation of knee joint MRI images is of great significance to reduce the work intensity of clinical doctors and nurses. In order to solve the problem that knee joint MRI image segmentation model needs a large number of high-quality tagged images and excessive labeling workload, a semisupervised learning segmentation network model based on 3D scSE-UNet is proposed. The model adopts a self-training semisupervised learning framework and adds a cSE-block+ module on the basis of the 3D UNet model. This module can enhance the effective features of the feature image from two aspects of space and channel, while suppressing irrelevant features and preserving image edge information more completely. In order to solve the problem of rough edge of pseudolabel caused by model segmentation, a fully connected conditional random field is added to refine the edge of pseudolabel in the process of model training. The effectiveness of the model is verified by open source MRNet dataset and OAI dataset. The results show that the proposed model can achieve the segmentation effect of fully supervised learning through a small number of labeled images and effectively reduce the dependence of knee joint MRI image segmentation on expert labeling data. © 2022 Jianjia Liu et al.

引用

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