Advertisement
Advances in Clinical Radiology

Musculoskeletal MR Image Segmentation with Artificial Intelligence

  • Elif Keles
    Affiliations
    Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University, Feinberg School of Medicine, 737 North Michigan Avenue Suite 1600, Chicago, IL 60611, USA
    Search for articles by this author
  • Ismail Irmakci
    Affiliations
    Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University, Feinberg School of Medicine, 737 North Michigan Avenue Suite 1600, Chicago, IL 60611, USA
    Search for articles by this author
  • Author Footnotes
    1 This work is partially supported by the NIH NCI funding: R01-CA246704 and R01-CA240639.
    Ulas Bagci
    Correspondence
    Corresponding author. Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University, Feinberg School of Medicine, 737 North Michigan Avenue Suite 1600, Chicago, IL 60611.
    Footnotes
    1 This work is partially supported by the NIH NCI funding: R01-CA246704 and R01-CA240639.
    Affiliations
    Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University, Feinberg School of Medicine, 737 North Michigan Avenue Suite 1600, Chicago, IL 60611, USA

    Department of Biomedical Engineering, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA

    Department of ECE, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
    Search for articles by this author
  • Author Footnotes
    1 This work is partially supported by the NIH NCI funding: R01-CA246704 and R01-CA240639.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Advances in Clinical Radiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Laur O.
        • Wang B.
        Musculoskeletal trauma and artificial intelligence: current trends and projections.
        Skeletal Radiol. 2022; 51: 257-269
        • Gorelik N.
        • Gyftopoulos S.
        Applications of artificial intelligence in musculoskeletal imaging: from the request to the report.
        Can Assoc Radiol J. 2021; 72: 45-59
        • Mutasa S.
        • Yi P.H.
        Clinical artificial intelligence applications: musculoskeletal.
        Radiol Clin North Am. 2021; 59: 1013-1026
        • Mutasa S.
        • Yi P.H.
        Deciphering musculoskeletal artificial intelligence for clinical applications: how do I get started?.
        Skeletal Radiol. 2022; 51: 271-278
        • Strohm L.
        • Hehakaya C.
        • Ranschaert E.R.
        • et al.
        Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors.
        Eur Radiol. 2020; 30: 5525-5532
        • Chea P.
        • Mandell J.C.
        Current applications and future directions of deep learning in musculoskeletal radiology.
        Skeletal Radiol. 2020; 49: 183-197
        • Del Grande F.
        • Guggenberger R.
        • Fritz J.
        Rapid Musculoskeletal MRI in 2021: value and Optimized Use of Widely Accessible Techniques.
        AJR Am J Roentgenol. 2021; 216: 704-717
        • Hirschmann A.
        • Cyriac J.
        • Stieltjes B.
        • et al.
        Artificial intelligence in musculoskeletal imaging: review of current literature, challenges, and trends.
        Semin Musculoskelet Radiol. 2019; 23: 304-311
        • Anwar S.M.
        • Irmakci I.
        • Torigian D.A.
        • et al.
        Semi-supervised deep learning for multi-tissue segmentation from multi-contrast MRI.
        J Signal Process Syst. 2022; 94: 497-510
        • LaLonde R.
        • Xu Z.
        • Irmakci I.
        • et al.
        Capsules for biomedical image segmentation.
        Med Image Anal. 2021; 68: 101889
        • Desai A.D.
        • Caliva F.
        • Iriondo C.
        • et al.
        The international workshop on osteoarthritis imaging knee MRI segmentation challenge: a multi-institute evaluation and analysis framework on a standardized dataset.
        Radiol Artif Intelligence. 2021; 3: e200078
        • Ferrucci L.
        The baltimore longitudinal study of aging (BLSA): a 50-year-long journey and plans for the future.
        J Gerontol Ser A Biol Sci Med Sci. 2008; 63: 1416-1419
        • Aurich V.
        • Weule J.
        Non-linear Gaussian filters performing edge preserving diffusion.
        in: Mustererkennung 1995. Springer, 1995: 538-545
        • Tustison N.J.
        • Avants B.B.
        • Cook P.A.
        • et al.
        N4ITK: Improved N3 Bias correction.
        IEEE Trans Med Imaging. 2010; 29: 1310-1320
        • Nyúl L.G.
        • Udupa J.K.
        • Zhang X.
        New variants of a method of MRI scale standardization.
        IEEE Trans Med Imaging. 2000; 19: 143-150
        • Souza A.
        • Udupa J.K.
        • Madabhushi A.
        Image filtering via generalized scale.
        Med image Anal. 2008; 12: 87-98
        • Madabhushi A.
        • Udupa J.K.
        New methods of MR image intensity standardization via generalized scale.
        Med Phys. 2006; 33: 3426-3434
        • Bağcı U.
        • Udupa J.K.
        • Bai L.
        The role of intensity standardization in medical image registration.
        Pattern Recognition Lett. 2010; 31: 315-323
        • Bagci U.
        • Chen X.
        • Udupa J.K.
        Hierarchical scale-based multiobject recognition of 3-D anatomical structures.
        IEEE Trans Med Imaging. 2011; 31: 777-789
        • Xu Z.
        • Gao M.
        • Papadakis G.Z.
        • et al.
        Joint solution for PET image segmentation, denoising, and partial volume correction.
        Med Image Anal. 2018; 46: 229-243
        • Ge Y.
        • Udupa J.K.
        • Nyul L.G.
        • et al.
        Numerical tissue characterization in MS via standardization of the MR image intensity scale.
        J Magn Reson Imaging. 2000; 12: 715-721
        • Ferrucci L.
        The Baltimore Longitudinal Study of Aging (BLSA): a 50-year-long journey and plans for the future.
        J Gerontol A Biol Sci Med Sci. 2008 Dec; 63: 1416-1419
        • Liu F.
        • Zhou Z.
        • Jang H.
        • et al.
        Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging.
        Magn Reson Med. 2018; 79: 2379-2391
        • Liu F.
        • Zhou Z.
        • Samsonov A.
        • et al.
        Deep learning approach for evaluating knee MR images: achieving high diagnostic performance for cartilage lesion detection.
        Radiology. 2018; 289: 160-169
        • Pedoia V.
        • Lee J.
        • Norman B.
        • et al.
        Diagnosing osteoarthritis from T2 maps using deep learning: an analysis of the entire Osteoarthritis Initiative baseline cohort.
        Osteoarthritis Cartilage. 2019; 27: 1002-1010
        • Badrinarayanan V.
        • Kendall A.
        • Cipolla R.
        Segnet: a deep convolutional encoder-decoder architecture for image segmentation.
        IEEE Trans Pattern Anal Mach Intell. 2017; 39: 2481-2495
      1. Heimann T, Morrison BJ, Styner MA, Niethammer M, Warfield S. Segmentation of knee images: a grand challenge. Paper presented at: Proc. MICCAI Workshop on Medical Image Analysis for the Clinic2010.

        • Tack A.
        • Mukhopadhyay A.
        • Zachow S.
        Knee menisci segmentation using convolutional neural networks: data from the osteoarthritis initiative.
        Osteoarthritis Cartilage. 2018; 26: 680-688
        • Deniz C.M.
        • Xiang S.
        • Hallyburton R.S.
        • et al.
        Segmentation of the proximal femur from MR images using deep convolutional neural networks.
        Sci Rep. 2018; 8: 1-14
        • Zhou Z.
        • Zhao G.
        • Kijowski R.
        • et al.
        Deep convolutional neural network for segmentation of knee joint anatomy.
        Magn Reson Med. 2018; 80: 2759-2770
      2. Mortazi A, Karim R, Rhode K, Burt J, Bagci U. CardiacNET: Segmentation of left atrium and proximal pulmonary veins from MRI using multi-view CNN. Paper presented at: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 2017. pp. 377-385.

        • Chaudhari A.S.
        • Fang Z.
        • Kogan F.
        • et al.
        Super-resolution musculoskeletal MRI using deep learning.
        Magn Reson Med. 2018; 80: 2139-2154
      3. Irmakci I, Anwar SM, Torigian DA, Bagci U. Deep learning for musculoskeletal image analysis. Paper presented at: 2019 53rd Asilomar Conference on Signals, Systems, and Computers2019.

        • Shin Y.
        • Kim S.
        • Lee Y.H.
        AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency?.
        Skeletal Radiol. 2022; 51: 293-304
        • Gorelik N.
        • Chong J.
        • Lin D.J.
        Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence.
        Semin Musculoskelet Radiol. 2020; 24: 38-49