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End-to-end deep learning model for segmentation and severity staging of anterior cruciate ligament injuries from MRI - 01/11/22

Doi : 10.1016/j.diii.2022.10.010 
Nguyen Tan Dung a, b, Ngo Huu Thuan c, d, Truong Van Dung a, Le Van Nho e, Nguyen Minh Tri f, g, Vu Pham Thao Vy g, h, Le Ngoc Hoang g, i, Nguyen Thuan Phat g, j, Dang Anh Chuong g, Luong Huu Dang k,
a Department of Rehabilitation, Da Nang C Hospital, Da Nang City 50000, Viet Nam 
b Department of Rehabilitation, Da Nang University of Medical Technology and Pharmacy, Da Nang City 50000, Viet Nam 
c Department of Radiology, Da Nang C Hospital, Da Nang city 50000, Viet Nam 
d Department of Medical Imaging, Da Nang University of Medical Technology and Pharmacy, Da Nang city, 50000, Viet Nam 
e Faculty of Medicine, Da Nang University of Medical Technology and Pharmacy, Da Nang City, 50000, Viet Nam 
f Advance Program in Computer Science, University of Science, Ho Chi Minh City 70000, Viet Nam 
g YRDx-AI Lab, Ho Chi Minh City 70000, Viet Nam 
h International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan 
i Graduate Institute of Biomedical Materials and Tissue Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan 
j Department of Computer Science, Vietnamese German University, Ho Chi Minh City 70000, Viet Nam 
k Department of Otolaryngology, Faculty of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City 70000, Viet Nam 

Corresponding author: luonghuudang167@ump.edu.vn
Sous presse. Épreuves corrigées par l'auteur. Disponible en ligne depuis le Tuesday 01 November 2022
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Hightlights

An end-to-end deep learning segmentation and classification model was built to identify anterior cruciate ligament tears on MRI.
The deep learning model (double-linear layers U-Net) combined with radiomic features yields high accuracy in classifying anterior cruciate ligament as intact (90% accuracy), partially torn (82% accuracy) or fully ruptured (92% accuracy).
The final model using pseudo data yields mean Dice similarity coefficients of 0.84 (intact anterior cruciate ligament [ACL]), 0.75 (partially torn ACL) and 0.73 (fully ruptured ACL), and root mean square errors of 0.06 (intact ACL), 0.05 (partially torn ACL) and 0.05 (fully ruptured ACL) in segmentation.

Le texte complet de cet article est disponible en PDF.

Abstract

Purpose

The purpose of this study was to develop a semi-supervised segmentation and classification deep learning model for the diagnosis of anterior cruciate ligament (ACL) tears on MRI based on a semi-supervised framework, double-linear layers U-Net (DCLU-Net).

Materials and methods

A total of 297 participants who underwent of total of 303 MRI examination of the knee with fat-saturated proton density (PD) fast spin-echo (FSE) sequence in the sagittal plane were included. There were 214 men and 83 women, with a mean age of 37.46 ± 1.40 (standard deviation) years (range: 29–44 years). Of these, 107 participants had intact ACL (36%), 98 had partially torn ACL (33%), and 92 had fully ruptured ACL (31%). The DCLU-Net was combined with radiomic features for enhancing performances in the diagnosis of ACL tear. The different evaluation metrics for both classification (accuracy, sensitivity, accuracy) and segmentation (mean Dice similarity coefficient and root mean square error) were compared individually for each image class across the three phases of the model, with each value being compared to its respective value from the previous phase. Findings at arthroscopic knee surgery were used as the standard of reference.

Results

With the addition of radiomic features, the final model yielded accuracies of 90% (95% CI: 83–92), 82% (95% CI: 73–86), and 92% (95% CI: 87–94) for classifying ACL as intact, partially torn and fully ruptured, respectively. The DCLU-Net achieved mean Dice similarity coefficient and root mean square error of 0.78 (95% CI: 0.71–0.80) and 0.05 (95% CI: 0.06–0.07), respectively, when segmenting the three ACL conditions with pseudo data (P < 0.001).

Conclusion

A dual-modules deep learning model with segmentation and classification capabilities was successfully developed. In addition, the use of semi-supervised techniques significantly reduced the amount of manual segmentation data without compromising performance.

Le texte complet de cet article est disponible en PDF.

Keywords : Anterior cruciate ligament tears, Classification, Knee injuries, Magnetic resonance imaging, Semi-supervised learning, Segmentation

Abbreviations : 3D, ACL, AUROC, BMI, CI, DCLU-Net, DL, FSE, MRI, PD, SD


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© 2022  Société française de radiologie. Publié par Elsevier Masson SAS. Tous droits réservés.
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