Automated knee MR images segmentation of anterior cruciate ligament tears

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dc.contributor.authorAwan, Mazhar Javed
dc.contributor.authorMohd Rahim, Mohd Shafry
dc.contributor.authorSalim, Naomie
dc.contributor.authorRehman, Amjad
dc.contributor.authorGarcía-Zapirain, Begoña
dc.date.accessioned2025-06-16T10:16:58Z
dc.date.available2025-06-16T10:16:58Z
dc.date.issued2022-02-17
dc.date.updated2025-06-16T10:16:58Z
dc.description.abstractThe 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.en
dc.description.sponsorshipUniversity of Deusto (eVIDA group, under the “Contrato Programa”) and “Hazitek” Program from Basque Governmenten
dc.identifier.citationAwan, M. J., Rahim, Salim, N., Rehman, A., & Garcia-Zapirain, B. (2022). Automated knee MR images segmentation of anterior cruciate ligament tears. Sensors, 22(4). https://doi.org/10.3390/S22041552
dc.identifier.doi10.3390/S22041552
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/20.500.14454/3068
dc.language.isoeng
dc.publisherMDPI
dc.rights© 2022 by the authors
dc.subject.otherACL MR images
dc.subject.otherArtificial intelligence
dc.subject.otherBiomedical images
dc.subject.otherConvolutional neural network
dc.subject.otherDeep learning
dc.subject.otherKnee bone
dc.subject.otherKnee mask
dc.subject.otherOsteoarthritis
dc.subject.otherPrediction
dc.subject.otherSegmentation
dc.subject.otherU-Net
dc.titleAutomated knee MR images segmentation of anterior cruciate ligament tearsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue4
oaire.citation.titleSensors
oaire.citation.volume22
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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