Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears

The anterior cruciate ligament (ACL) is one of the main stabilizer parts of the knee. ACL injury leads to causes of osteoarthritis risk. ACL rupture is common in the young athletic population. Accurate segmentation at an early stage can improve the analysis and classification of anterior cruciate li...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-02, Vol.22 (4), p.1552
Main Authors: Awan, Mazhar Javed, Rahim, Mohd Shafry Mohd, Salim, Naomie, Rehman, Amjad, Garcia-Zapirain, Begonya
Format: Article
Language:English
Subjects:
Accuracy
Anterior Cruciate Ligament
Anterior Cruciate Ligament Injuries - diagnostic imaging
Arthritis
artificial intelligence
biomedical images
Biomedical materials
Bones
Cartilage
Computer architecture
Datasets
Deep learning
Humans
Image compression
Image processing
Injuries
Joint and ligament injuries
Knee
Knee Joint
Ligaments
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Medical imaging
Medical imaging equipment
Neural networks
Osteoarthritis
Osteoporosis
segmentation
Semantics
Sports injuries
U-Net
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Summary:The anterior cruciate ligament (ACL) is one of the main stabilizer parts of the knee. ACL injury leads to causes of osteoarthritis risk. ACL rupture is common in the young athletic population. Accurate segmentation at an early stage can improve the analysis and classification of anterior cruciate ligaments tears. This study automatically segmented the anterior cruciate ligament (ACL) tears from magnetic resonance imaging through deep learning. The knee mask was generated on the original Magnetic Resonance (MR) images to apply a semantic segmentation technique with convolutional neural network architecture U-Net. The proposed segmentation method was measured by accuracy, intersection over union (IoU), dice similarity coefficient (DSC), precision, recall and F1-score of 98.4%, 99.0%, 99.4%, 99.6%, 99.6% and 99.6% on 11451 training images, whereas on the validation images of 3817 was, respectively, 97.7%, 93.8%,96.8%, 96.5%, 97.3% and 96.9%. We also provide dice loss of training and test datasets that have remained 0.005 and 0.031, respectively. The experimental results show that the ACL segmentation on JPEG MRI images with U-Nets achieves accuracy that outperforms the human segmentation. The strategy has promising potential applications in medical image analytics for the segmentation of knee ACL tears for MR images.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22041552