Ebrahimkhani, SomayehJaward, Mohamed HishamCicuttini, Flavia M.Dharmaratne, AnujaWang, YuanyuanGarcía Seco de Herrera, Alba2025-03-282025-03-282020-06Somayeh Ebrahimkhani, Mohamed Hisham Jaward, Flavia M. Cicuttini, Anuja Dharmaratne, Yuanyuan Wang, Alba G. Seco de Herrera, A review on segmentation of knee articular cartilage: from conventional methods towards deep learning, Artificial Intelligence in Medicine, Volume 106, 2020, 101851, ISSN 0933-3657, https://doi.org/10.1016/j.artmed.2020.101851.0933-3657; eISSN: 1873-2860https://doi.org/10.1016/J.ARTMED.2020.101851https://hdl.handle.net/20.500.14468/26387La versión registrada de este artículo, publicado por primera vez en Artificial Intelligence in Medicine, Volume 106, 2020, está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/J.ARTMED.2020.101851. The copyrighted version of this article, first published in Artificial Intelligence in Medicine, Volume 106, 2020, is available online at the publisher's website: Elsevier, https://doi.org/10.1016/J.ARTMED.2020.101851.In this paper, we review the state-of-the-art approaches for knee articular cartilage segmentation from conventional techniques to deep learning (DL) based techniques. Knee articular cartilage segmentation on magnetic resonance (MR) images is of great importance in early diagnosis of osteoarthritis (OA). Besides, segmentation allows estimating the articular cartilage loss rate which is utilised in clinical practice for assessing the disease progression and morphological changes. It has been traditionally applied in quantifying longitudinal knee OA progression pattern to detect and assess the articular cartilage thickness and volume. Topics covered include various image processing algorithms and major features of different segmentation techniques, feature computations and the performance evaluation metrics. This paper is intended to provide researchers with a broad overview of the currently existing methods in the field, as well as to highlight the shortcomings and potential considerations in the application at clinical practice. The survey showed that state-of-the-art techniques based on DL outperform the other segmentation methods. The analysis of the existing methods reveals that integration of DL-based algorithms with other traditional model-based approaches has achieved the best results (mean Dice similarity coefficient (DSC) between 85.8% and 90%).eninfo:eu-repo/semantics/openAccess12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 InformáticaartículoKnee osteoarthritis (OA)Articular cartilage segmentationMagnetic resonance imaging (MRI)Medical image analysisDeep convolutional neural network (CNN)