Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Medical Imaging
  4. Volume 20, Issue 1
  5. Article
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Using magnetic resonance imaging (MRI) in osteoarthritis pathogenesis research has proven extremely beneficial. However, it is always challenging for both clinicians and researchers to detect morphological changes in knee joints from magnetic resonance (MR) imaging since the surrounding tissues produce identical signals in MR studies, making it difficult to distinguish between them. Segmenting the knee bone, articular cartilage and menisci from the MR images allows one to examine the complete volume of the bone, articular cartilage, and menisci. It can also be used to assess certain characteristics quantitatively. However, segmentation is a laborious and time-consuming operation that requires sufficient training to complete correctly. With the advancement of MRI technology and computational methods, researchers have developed several algorithms to automate the task of individual knee bone, articular cartilage and meniscus segmentation during the last two decades. This systematic review aims to present available fully and semi-automatic segmentation methods for knee bone, cartilage, and meniscus published in different scientific articles. This review provides a vivid description of the scientific advancements to clinicians and researchers in this field of image analysis and segmentation, which helps the development of novel automated methods for clinical applications. The review also contains the recently developed fully automated deep learning-based methods for segmentation, which not only provides better results compared to the conventional techniques but also open a new field of research in Medical Imaging.

© 2024 Bentham Science Publishers
© 2024 The Author(s). Published by Bentham Science Publisher. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
Loading

Article metrics loading...

/content/journals/cmir/10.2174/1573405620666230515090557
2023-07-07
2024-11-23

  • From This Site

    /content/journals/cmir/10.2174/1573405620666230515090557
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

/deliver/fulltext/cmir/20/1/CMIR-20-E150523216894.html?itemId=/content/journals/cmir/10.2174/1573405620666230515090557&mimeType=html&fmt=ahah

References

  1. CastañedaS. Roman-BlasJ.A. LargoR. Herrero-BeaumontG. Subchondral bone as a key target for osteoarthritis treatment.Biochem. Pharmacol.201283331532310.1016/j.bcp.2011.09.01821964345
     [Google Scholar]
  2. GaitA.D. HodgsonR. ParkesM.J. HutchinsonC.E. O’NeillT.W. MaricarN. MarjanovicE.J. CootesT.F. FelsonD.T. Synovial volume vs synovial measurements from dynamic contrast enhanced MRI as measures of response in osteoarthritis.Osteoarthritis Cartilage20162481392139810.1016/j.joca.2016.03.01527038489
     [Google Scholar]
  3. BowesM.A. VincentG.R. WolstenholmeC.B. ConaghanP.G. A novel method for bone area measurement provides new insights into osteoarthritis and its progression.Ann Rheum Dis201374351925
     [Google Scholar]
  4. HunterD. NevittM. LynchJ. Longitudinal validation of periarticular bone area and 3D shape as biomarkers for knee OA progression? Data from the FNIH OA Biomarkers Consortium.Ann Rheum Dis2015759160714
     [Google Scholar]
  5. NeogiT. FelsonD.T. Bone as an imaging biomarker and treatment target in OA.Nat. Rev. Rheumatol.201612950350410.1038/nrrheum.2016.11327383914
     [Google Scholar]
  6. HeimannT. Segmentation of knee images: A grand challenge.Proc. MICCAIWorkshop on Medical Image Analysis for the Clinic2010
     [Google Scholar]
  7. Carmen TaylorJ. Comparison of quantitative imaging of cartilage for osteoarthritis: T2, T1ρ, dGEMRIC, and contrast-enhanced CT.Magn. Reson. Imaging200927677978410.1016/j.mri.2009.01.01619269769
     [Google Scholar]
  8. NeogiT. BowesM.A. NiuJ. De SouzaK.M. VincentG.R. GogginsJ. ZhangY. FelsonD.T. Magnetic resonance imaging-based three-dimensional bone shape of the knee predicts onset of knee osteoarthritis: data from the osteoarthritis initiative.Arthritis Rheum.20136582048205810.1002/art.3798723650083
     [Google Scholar]
  9. MacKayJ.W. Subchondral bone in osteoarthritis: Association between MRI texture analysis and histomorphometry.Osteoarthritis Cartilage201627986620
     [Google Scholar]
  10. AbabnehS.Y. PrescottJ.W. GurcanM.N. Automatic graph-cut based segmentation of bones from knee magnetic resonance images for osteoarthritis research.Med. Image Anal.201115443844810.1016/j.media.2011.01.00721474362
     [Google Scholar]
  11. Jose Gerardo Tamez-PenaS.T. Performance assessment of an automated segmentation system for knee MRI scans.ORS2016
     [Google Scholar]
  12. DamE.B. LillholmM. MarquesJ. NielsenM. Automatic segmentation of high- and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative.J. Med. Imaging (Bellingham)20152202400102400110.1117/1.JMI.2.2.02400126158096
     [Google Scholar]
  13. DodinP. Martel-PelletierJ. PelletierJ.P. AbramF. A fully automated human knee 3D MRI bone segmentation using the ray casting technique.Med. Biol. Eng. Comput.201149121413142410.1007/s11517‑011‑0838‑822038239
     [Google Scholar]
  14. BalsigerF. RonchettiT. PletscherM. Distal femur segmentation on MR images using random forests.Medical Image Analysis Laboratory2015
     [Google Scholar]
  15. WangQ. Semantic context forests for learning-based knee cartilage segmentation in 3D MR images.International MICCAI Workshop on Medical Computer Vision2013
     [Google Scholar]
  16. BindernagelM. KainmuellerD. SeimH. LameckerH. ZachowS. HegeHC. An articulated statistical shape model of the human knee.Bildverarbeitung für die Medizin 2011.SpringerBerlin, Heidelberg2011596310.1007/978‑3‑642‑19335‑4_14
     [Google Scholar]
  17. SeimH. KainmuellerD. LameckerH. BindernagelM. MalinowskiJ. ZachowS. Model-based Auto-Segmentation of Knee Bones and Cartilage in MRI Data.Auto-Segmentation of the Knee in MRI Data2010
     [Google Scholar]
  18. ShanL. ZachC. StynerM. CharlesC. NiethammerM. Automatic Bone Segmentation and Alignment From MR Knee Images.Proceedings of SPIE - The International Society for Optical Engineering201010.1117/12.841167
     [Google Scholar]
  19. RatzlaffC. GuermaziA. CollinsJ. KatzJ.N. LosinaE. VanwyngaardenC. RussellR. IranpourT. DuryeaJ. A rapid, novel method of volumetric assessment of MRI-detected subchondral bone marrow lesions in knee osteoarthritis.Osteoarthritis Cartilage201321680681410.1016/j.joca.2013.03.00723518154
     [Google Scholar]
  20. DenizC.M. XiangS. HallyburtonR.S. WelbeckA. BabbJ.S. HonigS. ChoK. ChangG. Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks.Sci. Rep.2018811648510.1038/s41598‑018‑34817‑630405145
     [Google Scholar]
  21. KimD. LeeJ. YoonJ.S. LeeK.J. WonK. Development of automated 3D knee bone segmentation with inhomogeneity correction for deformable approach in magnetic resonance imaging.RACS '18: Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems201828529010.1145/3264746.3264776
     [Google Scholar]
  22. ZhouZ. ZhaoG. KijowskiR. LiuF. Deep convolutional neural network for segmentation of knee joint anatomy.Magn. Reson. Med.20188062759277010.1002/mrm.2722929774599
     [Google Scholar]
  23. RoemerF. NeogiT. NevittM. FelsonD. ZhuY. ZhangY. LynchJ. JavaidM. CremaM. TornerJ. LewisC. GuermaziA. Subchondral bone marrow lesions are highly associated with, and predict subchondral bone attrition longitudinally: the MOST study.Osteoarthritis Cartilage200918147
     [Google Scholar]
  24. TiulpinA. ThevenotJ. RahtuE. LehenkariP. SaarakkalaS. Automatic knee osteoarthritis diagnosis from plain radiographs: A deep learning-based approach.Sci. Rep.201881172710.1038/s41598‑018‑20132‑729379060
     [Google Scholar]
  25. AmbellanF. TackA. EhlkeM. ZachowS. Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative.Med Image Anal20195210911810.1016/j.media.2018.11.009
     [Google Scholar]
  26. SchockJ. A method for semantic knee bone and cartilage segmentation with deep 3D shape fitting using data from the osteoarthritis initiative.Shape in Medical Imaging. ShapeMI 2020. Lecture Notes in Computer Science202012474
     [Google Scholar]
  27. RiniC. PerumalB. RajasekaranM.P. Automatic knee joint segmentation using Douglas-Rachford splitting method.Multimedia Tools Appl.2020799-106599662110.1007/s11042‑019‑08303‑8
     [Google Scholar]
  28. GattiA.A. MalyM.R. Automatic knee cartilage and bone segmentation using multi-stage convolutional neural networks: data from the osteoarthritis initiative.MAGMA202134685987510.1007/s10334‑021‑00934‑z34101071
     [Google Scholar]
  29. HeckelmanL.N. SoherB.J. SpritzerC.E. LewisB.D. DeFrateL.E. Design and validation of a semi-automatic bone segmentation algorithm from MRI to improve research efficiency.Sci. Rep.2022121782510.1038/s41598‑022‑11785‑635551485
     [Google Scholar]
  30. RiniC. PerumalB. Pallikonda RajasekaranM. MuneeswaranV. Automatic knee segmentation using eagle algorithm with multi stochastic objective process.3C Tecnología2021202133335310.17993/3ctecno.2021.specialissue8.333‑353
     [Google Scholar]
  31. RobertB. BoulangerP. Automatic Bone Segmentation from MRI for Real-Time Knee Tracking in Fluoroscopic Imaging.Diagnostics (Basel)2022129222810.3390/diagnostics1209222836140633
     [Google Scholar]
  32. PatekarR. KumarP.S. GanH-S. RamleeM.H. Automated knee bone segmentation and visualisation using mask RCNN and marching cube: Data from the osteoarthritis initiative.ASM Science Journal2022
     [Google Scholar]
  33. GandhamalA. TalbarS. GajreS. RazakR. HaniA.F.M. KumarD. Fully automated subchondral bone segmentation from knee MR images: Data from the Osteoarthritis Initiative.Comput. Biol. Med.20178811012510.1016/j.compbiomed.2017.07.00828711767
     [Google Scholar]
  34. ChenH. ZhaoN. TanT. KangY. SunC. XieG. VerdonschotN. SprengersA. Knee Bone and Cartilage Segmentation Based on a 3D Deep Neural Network Using Adversarial Loss for Prior Shape Constraint.Front. Med. (Lausanne)2022979290010.3389/fmed.2022.79290035669917
     [Google Scholar]
  35. FrippJ. CrozierS. WarfieldS.K. OurselinS. Automatic segmentation of the bone and extraction of the bone–cartilage interface from magnetic resonance images of the knee.Phys. Med. Biol.20075261617163110.1088/0031‑9155/52/6/00517327652
     [Google Scholar]
  36. FolkessonJ. DamE.B. OlsenO.F. PettersenP.C. ChristiansenC. Segmenting articular cartilage automatically using a voxel classification approach.IEEE Trans. Med. Imaging200726110611510.1109/TMI.2006.88680817243589
     [Google Scholar]
  37. WuD. SofkaM. BirkbeckN. ZhouS.K. Segmentation of multiple knee bones from CT for orthopedic knee surgery planning.Med Image Comput Comput Assist Interv201417Pt 13728010.1007/978‑3‑319‑10404‑1_47
     [Google Scholar]
  38. LeeS. ParkS.H. ShimH. YunI.D. LeeS.U. Optimization of local shape and appearance probabilities for segmentation of knee cartilage in 3-D MR images.Comput Vis Image Underst2011115121710172010.1016/j.cviu.2011.05.014
     [Google Scholar]
  39. LiangS. CharlesC. NiethammerM. Automatic multi-atlas-based cartilage segmentation from knee MR images.Biomedical Imaging201210281031
     [Google Scholar]
  40. ZhangK. LuW. MarzilianoP. Automatic knee cartilage segmentation from multi-contrast MR images using support vector machine classification with spatial dependencies.Magn Reson Imaging2013311017314310.1016/j.mri.2013.06.005
     [Google Scholar]
  41. LeeJ.-G. GumusS. MoonC.-H. KwohK. BaeK.T. Fully automated segmentation of cartilage from the MR images of knee using a multi-atlas and local structural analysis method.Med Phys201441909230310.1118/1.4893533
     [Google Scholar]
  42. ShanL. ZachC. CharlesC. NiethammerM. Automatic atlas-based three-label cartilage segmentation from MR knee images.Med. Image Anal.20141871233124610.1016/j.media.2014.05.00825128683
     [Google Scholar]
  43. PangJ. LiP. QiuM. ChenW. QiaoL. Automatic Articular Cartilage Segmentation Based on Pattern Recognition from Knee MRI Images.J. Digit. Imaging201528669570310.1007/s10278‑015‑9780‑x25700618
     [Google Scholar]
  44. ÖztürkC.N. AlbayrakS. Automatic segmentation of cartilage in high-field magnetic resonance images of the knee joint with an improved voxel-classification-driven region-growing algorithm using vicinity-correlated subsampling.Comput. Biol. Med.2016729010710.1016/j.compbiomed.2016.03.01127017069
     [Google Scholar]
  45. AlderinF. Automated Segmentation of the Meniscus. Examination work within the field of technology medical technology and the main area engineering physics, advanced level, 30 HP Stockholm, Sweden.2017
     [Google Scholar]
  46. NormanB. PedoiaV. MajumdarS. Use of 2D U-Net Convolutional Neural Networks for Automated Cartilage and Meniscus Segmentation of Knee MR Imaging Data to Determine Relaxometry and Morphometry.Radiology2018288117718510.1148/radiol.201817232229584598
     [Google Scholar]
  47. DodinP. AbramF. PelletierJ.P. Martel-PelletierJ. A fully automated system for quantification of knee bone marrow lesions using MRI and the osteoarthritis initiative cohort.J. Biomed. Graph. Comput.201231516510.5430/jbgc.v3n1p51
     [Google Scholar]
  48. AhnC. BuiT.D. LeeY. ShinJ. ParkH. Fully automated, level set-based segmentation for knee MRIs using an adaptive force function and template: data from the osteoarthritis initiative.Biomed. Eng. Online20161519910.1186/s12938‑016‑0225‑727558127
     [Google Scholar]
  49. BruiE. EfimtcevA.Y. FokinV.A. FernandezR. LevchukA.G. OgierA.C. SamsonovA.A. MatteiJ.P. MelchakovaI.V. BendahanD. AndreychenkoA. Deep learning-based fully automatic segmentation of wrist cartilage in MR images.NMR Biomed.2020338e432010.1002/nbm.432032394453
     [Google Scholar]
  50. LiuF. ZhouZ. SamsonovA. BlankenbakerD. LarisonW. KanarekA. LianK. KambhampatiS. KijowskiR. Deep learning approach for evaluating knee MR images: Achieving high diagnostic performance for cartilage lesion detection.Radiology2018289116016910.1148/radiol.201817298630063195
     [Google Scholar]
  51. LiuF. ZhouZ. JangH. SamsonovA. ZhaoG. KijowskiR. Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging: Deep Learning Approach for Segmenting MR Image.Magn Reson Med201879(2).10.1002/mrm.26841
     [Google Scholar]
  52. LiuF. SUSAN: Segment unannotated image structure using adversarial network.Magn. Reson. Med.20198153330334530536427
     [Google Scholar]
  53. KashyapS. ZhangH. SonkaM. Just-enough interaction approach to knee MRI segmentation: Data from the osteoarthritis initiative.arXiv:1903.04027v12019
     [Google Scholar]
  54. BonarettiS. GoldG.E. BeaupreG.S. pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage.PLoS One2019151e0226501
     [Google Scholar]
  55. KashyapS. OguzI. ZhangH. SonkaM. Automated segmentation of knee MRI using hierarchical classifiers and just enough interaction based learning: Data from osteoarthritis initiative.Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. MICCAI 2016. Lecture Notes in Computer Science20199901
     [Google Scholar]
  56. KashyapS. ZhangH. RaoK. SonkaM. Learning-based cost functions for 3D and 4D multi-surface multi-object segmentation of Knee MRI: Data from the osteoarthritis initiative.IEEE Transactions on Medical Imaging20183751103111310.1109/TMI.2017.2781541
     [Google Scholar]
  57. FrippJ. CrozierS. WarfieldS.K. OurselinS. Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee.IEEE Trans. Med. Imaging2010291556410.1109/TMI.2009.202474319520633
     [Google Scholar]
  58. DodinP. PelletierJ. Martel-PelletierJ. AbramF. Automatic Human Knee Cartilage Segmentation From 3-D Magnetic Resonance Images.IEEE Trans. Biomed. Eng.201057112699271110.1109/TBME.2010.2058112
     [Google Scholar]
  59. Ali ShahS.A. YahyaK.M. MubasharG. BaisA. Quantification and visualization of MRI cartilage of the knee: A simplified approach.2010 6th International Conference on Emerging Technologies (ICET)201010.1109/ICET.2010.5638495
     [Google Scholar]
  60. Mallikarjuna SwamyM.S. HoliM.S. Knee joint cartilage visualization and quantification in normal and osteoarthritis.2010 International Conference on Systems in Medicine and Biology201010.1109/ICSMB.2010.5735360
     [Google Scholar]
  61. LongN.Q. JiangD. DingC. “Application of artificial neural networks in automatic cartilage segmentation,” 3rd Int.Work. Adv. Comput. Intell. IWACI201020108185
     [Google Scholar]
  62. WilliamsT.G. Automatic segmentation of bones and inter-image anatomical correspondence by volumetric statistical modelling of knee MRI.2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro201010.1109/ISBI.2010.5490316
     [Google Scholar]
  63. Yin Yin Xiangmin Zhang WilliamsR. Xiaodong Wu AndersonD.D. SonkaM. Logismos layered optimal graph image segmentation of multiple objects and surfaces: Cartilage segmentation in the knee joint.IEEE Trans. Med. Imaging201029122023203710.1109/TMI.2010.205886120643602
     [Google Scholar]
  64. Tamez-PenaJ. GonzálezP. FarberJ. BaumK. SchreyerE. TottermanS. Atlas based method for the automated segmentation and quantification of knee features: Data from the osteoarthritis initiativeInt. Symp. Biomed. Imaging14841487201110.1109/ISBI.2011.5872681
     [Google Scholar]
  65. ZhangK. DengJ. LuW. Segmenting human knee cartilage automatically from multi-contrast MR images using support vector machines and discriminative random fields.2011 18th IEEE International Conference on Image Processing201110.1109/ICIP.2011.6116655
     [Google Scholar]
  66. JiangJ-G. Segmentation of knee joints based on improved multiphase Chan-Vese model.2008 2nd International Conference on Bioinformatics and Biomedical Engineering2008
     [Google Scholar]
  67. MarstalK. GudbergsenH. BoesenM. KubassovaO. BouertR. BliddalH. Semi-automatic segmentation of knee osteoarthritic cartilage in magnetic resonance images.Proceedings ELMAR-20112011385388
     [Google Scholar]
  68. TranH.V. JiangD. Articular cartilage segmentation in noisy MR images of human knee2012 Cairo International Biomedical Engineering Conference (CIBEC)146149201210.1109/CIBEC.2012.6473331
     [Google Scholar]
  69. Tamez-PeñaJ.G. FarberJ. GonzálezP.C. SchreyerE. SchneiderE. TottermanS. Unsupervised segmentation and quantification of anatomical knee features: data from the Osteoarthritis Initiative.IEEE Trans. Biomed. Eng.20125941177118610.1109/TBME.2012.218661222318477
     [Google Scholar]
  70. KashyapS. YinY. SonkaM. Automated analysis of cartilage morphology2013 IEEE 10th International Symposium on Biomedical Imaging130013032013
     [Google Scholar]
  71. PrasoonA. PetersenK. IgelC. LauzeF. DamE. NielsenM. Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network.International conference on medical image computing and computer-assisted intervention201324625310.1007/978‑3‑642‑40763‑5_31
     [Google Scholar]
  72. PrasoonA. IgelC. LoogM. LauzeF. DamE.B. NielsenM. Femoral cartilage segmentation in Knee MRI scans using two stage voxel classification.2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)201310.1109/EMBC.2013.6610787
     [Google Scholar]
  73. ShanL. CharlesC. NiethammerM. Longitudinal three-label segmentation of knee cartilage.2013 IEEE 10th International Symposium on Biomedical Imaging2013
     [Google Scholar]
  74. GanH.S. TanT.S. SayutiK.A. KarimA.H.A. KadirM.R.A. Multilabel graph based approach for knee cartilage segmentation: Data from the osteoarthritis initiative.2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES)201421021310.1109/IECBES.2014.7047487
     [Google Scholar]
  75. KubicekJ. PenhakerM. Fuzzy algorithm for segmentation of images in extraction of objects from MRI.2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)20141422142710.1109/ICACCI.2014.6968264
     [Google Scholar]
  76. GanH.S. Binary Seeds Auto Generation Model for Knee Cartilage Segmentation.2018 International Conference on Intelligent and Advanced System (ICIAS)201810.1109/ICIAS.2018.8540570
     [Google Scholar]
  77. RevathiS.A. HoliG. Cartilage Segmentation of Knee OsteoArthritis from Magnetic Resonance Images(MRI).2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC)2018
     [Google Scholar]
  78. VikenH. AngmanM. Automatic segmentation of articular cartilage in arthroscopic images using deep neural networks and multifractal analysis. Linköping University Department of Biomedical Engineering Master’s thesis, 30 ECTS Computer science.2020
     [Google Scholar]
  79. XueY.P. JangH. ByraM. CaiZ.Y. WuM. ChangE.Y. MaY.J. DuJ. Automated cartilage segmentation and quantification using 3D ultrashort echo time (UTE) cones MR imaging with deep convolutional neural networks.Eur. Radiol.202131107653766310.1007/s00330‑021‑07853‑633783571
     [Google Scholar]
  80. DimitriA. MacKayJW. McDonnellS.M. Segmentation of knee MRI data with convolutional neural networks for semi-automated three-dimensional surface-based analysis of cartilage morphology and composition.Osteoarthritis Imaging202222100010
     [Google Scholar]
  81. YangM. ColakC. ChundruK.K. GajS. NanavatiA. JonesM.H. WinalskiC.S. SubhasN. LiX. Automated knee cartilage segmentation for heterogeneous clinical MRI using generative adversarial networks with transfer learning.Quant. Imaging Med. Surg.20221252620263310.21037/qims‑21‑45935502381
     [Google Scholar]
  82. StehlingC. BaumT. Mueller-HoeckerC. LieblH. Carballido-GamioJ. JosephG.B. MajumdarS. LinkT.M. A novel fast knee cartilage segmentation technique for T2 measurements at MR imaging – data from the Osteoarthritis Initiative.Osteoarthritis Cartilage201119898498910.1016/j.joca.2011.04.00221515391
     [Google Scholar]
  83. FrondeliusT. TiulpinA. LehenkariP. NieminenH.J. SaarakkalaS. Fully automatic deep learning based segmentation of bone-cartilage interface from micro-CT images of human osteochondral samples.Osteoarthr. Cartil.2018261S46910.1016/j.joca.2018.02.885
     [Google Scholar]
  84. WirthW. MaschekS. BeringerP. EcksteinF. Subregional laminar cartilage MR spin–spin relaxation times (T2) in osteoarthritic knees with and without medial femorotibial cartilage loss – data from the Osteoarthritis Initiative (OAI).Osteoarthr. Cartil.20172581313132310.1016/j.joca.2017.03.01328351705
     [Google Scholar]
  85. SiLiping XuanKai ZhongJingyu HuoJiayu XingYue GengJia HuYangfan ZhangHuan WangQian YaoWeiwu Knee cartilage thickness differs alongside ages: A 3-T magnetic resonance research upon 2,481 subjects via deep learning.Front Med2021760004910.3389/fmed.2020.600049
     [Google Scholar]
  86. AlmajalidR. ShanJ. DuY. ZhangM. Identification of Knee Cartilage Changing Pattern.Appl. Sci. (Basel)2019917346910.3390/app9173469
     [Google Scholar]
  87. PanfilovEgor Deep learning-based segmentation of knee MRI for fully automatic subregional morphological assessment of cartilage tissues: Data from the osteoarthritis initiative.J Orthop Res202140511131124
     [Google Scholar]
  88. SekiyaI. KohnoY. HyodoA. KatanoH. KomoriK. KogaH. TomitaM. SuzukiK. MasumotoJ. OzekiN. Interscan measurement error of knee cartilage thickness and projected cartilage area ratio at 9 regions and 45 subregions by fully automatic three-dimensional MRI analysis.Eur. J. Radiol.202113910970010.1016/j.ejrad.2021.10970033865065
     [Google Scholar]
  89. EnglundM. GuermaziA. RoemerF.W. AliabadiP. YangM. LewisC.E. TornerJ. NevittM.C. SackB. FelsonD.T. Meniscal tear in knees without surgery and the development of radiographic osteoarthritis among middle-aged and elderly persons: The multicenter osteoarthritis study.Arthritis Rheum.200960383183910.1002/art.2438319248082
     [Google Scholar]
  90. HunterD.J. ZhangY.Q. NiuJ.B. TuX. AminS. ClancyM. GuermaziA. GrigorianM. GaleD. FelsonD.T. The association of meniscal pathologic changes with cartilage loss in symptomatic knee osteoarthritis.Arthritis Rheum.200654379580110.1002/art.2172416508930
     [Google Scholar]
  91. SharmaL. EcksteinF. SongJ. GuermaziA. PrasadP. KapoorD. CahueS. MarshallM. HudelmaierM. DunlopD. Relationship of meniscal damage, meniscal extrusion, malalignment, and joint laxity to subsequent cartilage loss in osteoarthritic knees.Arthritis Rheum.20085861716172610.1002/art.2346218512777
     [Google Scholar]
  92. PaprokiA. EngstromC. ChandraS.S. NeubertA. FrippJ. CrozierS. Automated segmentation and analysis of normal and osteoarthritic knee menisci from magnetic resonance images – data from the Osteoarthritis Initiative.Osteoarthritis Cartilage20142291259127010.1016/j.joca.2014.06.02925014660
     [Google Scholar]
  93. TackA. MukhopadhyayA. ZachowS. Knee menisci segmentation using convolutional neural networks: data from the Osteoarthritis Initiative.Osteoarthritis Cartilage201826568068810.1016/j.joca.2018.02.90729526784
     [Google Scholar]
  94. BloeckerK. WirthW. HudelmaierM. BurgkartR. FrobellR. EcksteinF. Morphometric differences between the medial and lateral meniscus in healthy men - a three-dimensional analysis using magnetic resonance imaging.Cells Tissues Organs2012195435336410.1159/00032701221709397
     [Google Scholar]
  95. ZarandiM.H.F. KhadangiA. KarimiF. TurksenI.B. A Computer-Aided Type-II Fuzzy Image Processing for Diagnosis of Meniscus Tear.J. Digit. Imaging201629667769510.1007/s10278‑016‑9884‑y27198133
     [Google Scholar]
  96. ZhangK. LiL. YangL. Effect of degenerative and radial tears of the meniscus and resultant meniscectomy on the knee joint: A finite element analysis.J Orthop Translat201918202110.1016/j.jot.2018.12.004
     [Google Scholar]
  97. CouteauxV. Si-MohamedS. NempontO. LefevreT. PopoffA. PizaineG. VillainN. BlochI. CottenA. BousselL. Automatic knee meniscus tear detection and orientation classification with Mask-RCNN.Diagn. Interv. Imaging2019100423524210.1016/j.diii.2019.03.00230910620
     [Google Scholar]
  98. ByraM. WuM. ZhangX. JangH. MaY.J. ChangE.Y. ShahS. DuJ. Knee menisci segmentation and relaxometry of 3D ultrashort echo time cones MR imaging using attention U-Net with transfer learning.Magn. Reson. Med.20208331109112210.1002/mrm.2796931535731
     [Google Scholar]
  99. FritzB. MarbachG. CivardiF. FucenteseS.F. PfirrmannC.W.A. Deep convolutional neural network-based detection of meniscus tears: comparison with radiologists and surgery as standard of reference.Skeletal Radiol.20204981207121710.1007/s00256‑020‑03410‑2
     [Google Scholar]
  100. LongZ. ZhangD. GuoH. WangW. Automated segmentation of knee menisci from magnetic resonance images by using ATTU-Net: a pilot study on small datasets.OSA Continuum20214123096310710.1364/OSAC.444518
     [Google Scholar]
  101. WangY. LiY. HuangM. LaiQ. HuangJ. ChenJ. Feasibility of Constructing an Automatic Meniscus Injury Detection Model Based on Dual-Mode Magnetic Resonance Imaging (MRI) Radiomics of the Knee Joint.Comput. Math. Methods Med.2022202211310.1155/2022/215513235392588
     [Google Scholar]
  102. Radiological Society of North America 2013 Scientific Assembly and Annual Meeting, Chicago IL.2013 Available from: https://www.medscape.com/viewcollection/32980
  103. PatelR. EltgrothM. SouzaR.B. ZhangC.A. MajumdarS. LinkT.M. MotamediD. Loaded versus unloaded magnetic resonance imaging (MRI) of the knee: Effect on meniscus extrusion in healthy volunteers and patients with osteoarthritis.Eur. J. Radiol. Open2016310010710.1016/j.ejro.2016.05.00227331081
     [Google Scholar]
  104. OzekiN. SeilR. KrychA.J. KogaH. Surgical treatment of complex meniscus tear and disease: state of the art.J. ISAKOS202161354510.1136/jisakos‑2019‑00038033833044
     [Google Scholar]
  105. LorigoL.M. FaugerasO. GrimsonW.E.L. KerivenR. KikinisR. Segmentation of bone in clinical knee MRI using texture-based geodesic active contoursInternational Conference on Medical Image Computing and Computer-Assisted Intervention19981195120410.1007/BFb0056309
     [Google Scholar]
  106. CohenZ.A. McCarthyD.M. KwakS.D. LegrandP. FogarasiF. CiaccioE.J. AteshianG.A. Knee cartilage topography, thickness, and contact areas from MRI: in-vitro calibration and in-vivo measurements.Osteoarthritis Cartilage1999719510910.1053/joca.1998.016510367018
     [Google Scholar]
  107. LynchJ. A. ZaimS. ZhaoJ. StorkA. PeterfyC. G. GenantH. K. Cartilage segmentation of 3D MRI scans of the osteoarthritic knee combining user knowledge and active contours.Proceedings of SPIE - The International Society for Optical Engineering200010.1117/12.387758
     [Google Scholar]
  108. Carballido-GamioJ. BauerJ.S. Keh-Yang Lee KrauseS. MajumdarS. Combined image processing techniques for characterization of MRI cartilage of the knee.Conf. Proc. IEEE Eng. Med. Biol. Soc.200520053043304610.1109/IEMBS.2005.161711617282885
     [Google Scholar]
  109. Carballido-GamioJ. BauerJ.S. StahlR. LeeK.Y. KrauseS. LinkT.M. MajumdarS. Inter-subject comparison of MRI knee cartilage thickness.Med. Image Anal.200812212013510.1016/j.media.2007.08.00217923429
     [Google Scholar]
  110. Jinshan Tang MillingtonS. ActonS.T. CrandallJ. HurwitzS. Surface extraction and thickness measurement of the articular cartilage from MR images using directional gradient vector flow snakes.IEEE Trans. Biomed. Eng.200653589690710.1109/TBME.2006.87281616686412
     [Google Scholar]
  111. SollowayS. HutchinsonC.E. WatertonJ.C. TaylorC.J. The use of active shape models for making thickness measurements of articular cartilage from MR images.Magn. Reson. Med.199737694395210.1002/mrm.19103706209178247
     [Google Scholar]
  112. GillesB. Magnenat-ThalmannN. Musculoskeletal MRI segmentation using multi-resolution simplex meshes with medial representations.Med. Image Anal.201014329130210.1016/j.media.2010.01.00620303319
     [Google Scholar]
  113. VincentG. WolstenholmeC. ScottI. BowesM. Fully automatic segmentation of the knee joint using active appearance models.2010
     [Google Scholar]
  114. WilliamsT.G. TaylorC.J. GaoZ. WatertonJ.C. Corresponding articular cartilage thickness measurements in the knee joint by modelling the underlying bone (commercial in confidenceBiennial International Conference on Information Processing in Medical Imaging200312613510.1007/978‑3‑540‑45087‑0_11
     [Google Scholar]
  115. WilliamsT.G. TaylorC.J. WatertonJ.C. HolmesA. Population analysis of knee cartilage thickness maps using model based correspondence.IEEE Xplore200410.1109/ISBI.2004.1398507
     [Google Scholar]
  116. WangZ. DonoghueC. RueckertD. Patch-based segmentation without registration: application to knee MRIInternational Workshop on Machine Learning in Medical Imaging20139810510.1007/978‑3‑319‑02267‑3_13
     [Google Scholar]
  117. LiuQ. WangQ. ZhangL. GaoY. ShenD. Multi-atlas context forests for knee MR image segmentationInternational Workshop on Machine Learning in Medical Imaging201518619310.1007/978‑3‑319‑24888‑2_23
     [Google Scholar]
  118. SchmidJ. Magnenat-ThalmannN. MRI bone segmentation using deformable models and shape priorsInternational conference on medical image computing and computer-assisted intervention200811912610.1007/978‑3‑540‑85988‑8_15
     [Google Scholar]
  119. SchmidJ. KimJ. Magnenat-ThalmannN. Robust statistical shape models for MRI bone segmentation in presence of small field of view.Med. Image Anal.201115115516810.1016/j.media.2010.09.00120951075
     [Google Scholar]
  120. PrasoonA. IgelC. LoogM. LauzeF. DamE. NielsenM. Cascaded classifier for large-scale data applied to automatic segmentation of articular cartilage.Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 83144V201210.1117/12.910809
     [Google Scholar]
  121. HuangC. ShanL. CharlesH.C. WirthW. NiethammerM. ZhuH. Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data.IEEE Trans. Med. Imaging20153491914192710.1109/TMI.2015.241567525823031
     [Google Scholar]
  122. EcksteinF. WirthW. NevittM.C. Recent advances in osteoarthritis imaging—the Osteoarthritis Initiative.Nat. Rev. Rheumatol.201281062263010.1038/nrrheum.2012.11322782003
     [Google Scholar]
  123. ZerfassP. LowitzT. MuseykoO. BoussonV. LaouissetL. KalenderW.A. LaredoJ.D. EngelkeK. An integrated segmentation and analysis approach for QCT of the knee to determine subchondral bone mineral density and texture.IEEE Trans. Biomed. Eng.20125992449245810.1109/TBME.2012.220266022692866
     [Google Scholar]
  124. YoussefR. BouhadounH. LaredoJ.D. ChappardC. Semi-automatic compartment extraction to assess 3D bone mineral density and morphometric parameters of the subchondral bone in the tibial knee 2015 19th International Conference on Information Visualisation201551852310.1109/iV.2015.92
     [Google Scholar]
  125. LeeH. HongH. KimJ. BCD-NET: A novel method for cartilage segmentation of knee MRI via deep segmentation networks with bone-cartilage-complex modeling2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)201810.1109/ISBI.2018.8363866
     [Google Scholar]
  126. ThahaR. JogiS.P. RajanS. MahajanV. MehndirattaA. SinghA. Automated Segmentation of Knee Cartilage Using Modified Radial Approach for OA Patients with and without Bone Abnormality2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)201843243610.1109/IECBES.2018.8626718
     [Google Scholar]
  127. UozumiY. NagamuneK. An automatic bone segmentation method based on anatomical structure for the knee joint in MDCT image2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)20137124712710.1109/EMBC.2013.6611200
     [Google Scholar]
  128. DesaiP.R. HacihalilogluI. Enhancement and automated segmentation of ultrasound knee cartilage for early diagnosis of knee osteoarthritis 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)20181471147410.1109/ISBI.2018.8363850
     [Google Scholar]
  129. YamamotoY. TsurutaS. KobashiS. SakuraiY. KnaufR. An Efficient Classification Method for Knee MR Image Segmentation 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)2016364110.1109/SITIS.2016.15
     [Google Scholar]
  130. SureshaS. KidzińskiL. HalilajE. GoldG.E. DelpS.L. Automated staging of knee osteoarthritis severity using deep neural networks.Osteoarthr. Cartil.2018261S44110.1016/j.joca.2018.02.845
     [Google Scholar]
  131. JosephG.B. BaumT. AlizaiH. Carballido-GamioJ. NardoL. VirayavanichW. LynchJ.A. NevittM.C. McCullochC.E. MajumdarS. LinkT.M. Baseline mean and heterogeneity of MR cartilage T2 are associated with morphologic degeneration of cartilage, meniscus, and bone marrow over 3years – data from the Osteoarthritis Initiative.Osteoarthr. Cartil.201220772773510.1016/j.joca.2012.04.00322503812
     [Google Scholar]
  132. BhagyashriL.W. PatilM.M. Osteoarthritis disease detection with the help of image processing technique.Int. J. Comput. Appl.201514
     [Google Scholar]
  133. BeufO. Characterization of trabecular bone micro-architecture in the knee in osteoarthrosis using high-resolution MRI.Clin. Rheumatol.19954321638816388
     [Google Scholar]
  134. StammbergerT. EcksteinF. EnglmeierK.H. ReiserM. Determination of 3D cartilage thickness data from MR imaging: Computational method and reproducibility in the living.Magn. Reson. Med.199941352953610.1002/(SICI)1522‑2594(199903)41:3<529::AID‑MRM15>3.0.CO;2‑Z10204876
     [Google Scholar]
  135. StammbergerT. HoheJ. EnglmeierK.H. ReiserM. EcksteinF. Elastic registration of 3D cartilage surfaces from MR image data for detecting local changes in cartilage thickness.Magn. Reson. Med.200044459260110.1002/1522‑2594(200010)44:4<592::AID‑MRM13>3.0.CO;2‑J11025515
     [Google Scholar]
  136. WilliamsT.G. HolmesA.P. WatertonJ.C. MaciewiczR.A. HutchinsonC.E. MootsR.J. NashA F P. TaylorC.J. Anatomically corresponded regional analysis of cartilage in asymptomatic and osteoarthritic knees by statistical shape modelling of the bone.IEEE Trans. Med. Imaging20102981541155910.1109/TMI.2010.204765320378463
     [Google Scholar]
  137. KauffmannC. GravelP. GodboutB. GravelA. BeaudoinG. RaynauldJ. Martel-PelletierJ. PelletierJ. de GuiseJ.A. Computer-aided method for quantification of cartilage thickness and volume changes using mri: validation study using a synthetic model.IEEE Trans. Biomed. Eng.200350897898810.1109/TBME.2003.81453912892325
     [Google Scholar]
  138. SwansonM.S. PrescottJ.W. BestT.M. PowellK. JacksonR.D. HaqF. GurcanM.N. Semi-automated segmentation to assess the lateral meniscus in normal and osteoarthritic knees.Osteoarthritis Cartilage201018334435310.1016/j.joca.2009.10.00419857510
     [Google Scholar]
  139. FolkessonJ. DamE. OlsenO.F. PettersenP. ChristiansenC. Automatic segmentation of the articular cartilage in knee MRI using a hierarchical multi-class classification schemeInternational Conference on Medical Image Computing and Computer-Assisted Intervention200532733410.1007/11566465_41
     [Google Scholar]
  140. FolkessonJ. OlsenO.F. PettersenP. DamE. ChristiansenC. Combining binary classifiers for automatic cartilage segmentation in knee MRIInternational Workshop on Computer Vision for Biomedical Image Applications200523023910.1007/11569541_24
     [Google Scholar]
  141. GornaleS.S. PatravaliP.U. MaratheK.S. HiremathP.S. “Determination of Osteoarthritis Using Histogram of Oriented Gradients and Multiclass SVM,” Int. J. Image.Graph. Signal Process.2017912414910.5815/ijigsp.2017.12.05
     [Google Scholar]
  142. AngI. FoxM. PolkJ.D. KershM.E. A structure-based algorithm for automated separation of subchondral bone in micro-computed tomography data.Preprint201810.31224/osf.io/w86ke
     [Google Scholar]
  143. LiY.Z. WangY. FangK.B. ZhengH.Z. LaiQ.Q. XiaY.F. ChenJ.Y. DaiZ. Automated meniscus segmentation and tear detection of knee MRI with a 3D mask-RCNN.Eur. J. Med. Res.202227124710.1186/s40001‑022‑00883‑w36372871
     [Google Scholar]
  144. FrippJ. BourgeatP. MewesA.J. WarfieldS.K. CrozierS. OurselinS. 3D statistical shape models to embed spatial relationship informationInternational Workshop on Computer Vision for Biomedical Image Applications2005516010.1007/11569541_7
     [Google Scholar]
  145. LitjensG. TothR. van de VenW. HoeksC. KerkstraS. van GinnekenB. VincentG. GuillardG. BirbeckN. ZhangJ. StrandR. MalmbergF. OuY. DavatzikosC. KirschnerM. JungF. YuanJ. QiuW. GaoQ. EdwardsP.E. MaanB. van der HeijdenF. GhoseS. MitraJ. DowlingJ. BarrattD. HuismanH. MadabhushiA. Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge.Med. Image Anal.201418235937310.1016/j.media.2013.12.00224418598
     [Google Scholar]
  146. JanvierT. ToumiH. JennaneR. LespessaillesE. Subchondral tibial bone texture predicts the incidence of radiographic knee osteoarthritis: Data from the osteoarthritis initiative.Osteoarthr. Cartil.2017251220472054
     [Google Scholar]
  147. MalyM.R. AckerS.M. TottermanS. Tamez-PeñaJ. StratfordP.W. CallaghanJ.P. AdachiJ.D. BeattieK.A. Knee adduction moment relates to medial femoral and tibial cartilage morphology in clinical knee osteoarthritis.J. Biomech.201548123495350110.1016/j.jbiomech.2015.04.03926141161
     [Google Scholar]
  148. HossainM.B. LaiK.W. Pingguan-MurphyB. HumY.C. Mohd SalimM.I. LiewY.M. Contrast enhancement of ultrasound imaging of the knee joint cartilage for early detection of knee osteoarthritis.Biomed. Signal Process. Control201413115716710.1016/j.bspc.2014.04.008
     [Google Scholar]
/content/journals/cmir/10.2174/1573405620666230515090557
Loading
A Comprehensive Review on MRI-based Knee Joint Segmentation and Analysis Techniques
Curr. Med. Imaging 20, e150523216894 (2024); https://doi.org/10.2174/1573405620666230515090557
/content/journals/cmir/10.2174/1573405620666230515090557
/content/journals/cmir/10.2174/1573405620666230515090557
Loading

Data & Media loading...


  • Article Type:     Review Article
Keyword(s): Automated segmentation; Bone; Cartilage; Knee Joint; Magnetic Resonance Image (MRI); Meniscus; Osteoarthritis (OA)

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test