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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8876207/ https://www.ncbi.nlm.nih.gov/pubmed/35214451 http://dx.doi.org/10.3390/s22041552 |
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author | Awan, Mazhar Javed Rahim, Mohd Shafry Mohd Salim, Naomie Rehman, Amjad Garcia-Zapirain, Begonya |
author_facet | Awan, Mazhar Javed Rahim, Mohd Shafry Mohd Salim, Naomie Rehman, Amjad Garcia-Zapirain, Begonya |
author_sort | Awan, Mazhar Javed |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8876207 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88762072022-02-26 Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears Awan, Mazhar Javed Rahim, Mohd Shafry Mohd Salim, Naomie Rehman, Amjad Garcia-Zapirain, Begonya Sensors (Basel) Article 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. MDPI 2022-02-17 /pmc/articles/PMC8876207/ /pubmed/35214451 http://dx.doi.org/10.3390/s22041552 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Awan, Mazhar Javed Rahim, Mohd Shafry Mohd Salim, Naomie Rehman, Amjad Garcia-Zapirain, Begonya Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears |
title | Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears |
title_full | Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears |
title_fullStr | Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears |
title_full_unstemmed | Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears |
title_short | Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears |
title_sort | automated knee mr images segmentation of anterior cruciate ligament tears |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8876207/ https://www.ncbi.nlm.nih.gov/pubmed/35214451 http://dx.doi.org/10.3390/s22041552 |
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