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Mahendrakar P, Kumar D, Patil U. A Comprehensive Review on MRI-based Knee Joint Segmentation and Analysis Techniques. Curr Med Imaging 2024; 20:e150523216894. [PMID: 37189281 DOI: 10.2174/1573405620666230515090557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/29/2022] [Accepted: 12/28/2022] [Indexed: 05/17/2023]
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.
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Affiliation(s)
- Pavan Mahendrakar
- BLDEA’s V.P.Dr. P.G., Halakatti College of Engineering and Technology, Vijayapur, Karnataka, India
| | | | - Uttam Patil
- Jain College of Engineering, T.S Nagar, Hunchanhatti Road, Machhe, Belagavi, Karnataka, India
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Ebrahimkhani S, Dharmaratne A, Jaward MH, Wang Y, Cicuttini FM. Automated segmentation of knee articular cartilage: Joint deep and hand-crafted learning-based framework using diffeomorphic mapping. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.09.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Automated cartilage segmentation and quantification using 3D ultrashort echo time (UTE) cones MR imaging with deep convolutional neural networks. Eur Radiol 2021; 31:7653-7663. [PMID: 33783571 DOI: 10.1007/s00330-021-07853-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 12/18/2020] [Accepted: 01/15/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE To develop a fully automated full-thickness cartilage segmentation and mapping of T1, T1ρ, and T2*, as well as macromolecular fraction (MMF) by combining a series of quantitative 3D ultrashort echo time (UTE) cones MR imaging with a transfer learning-based U-Net convolutional neural networks (CNN) model. METHODS Sixty-five participants (20 normal, 29 doubtful-minimal osteoarthritis (OA), and 16 moderate-severe OA) were scanned using 3D UTE cones T1 (Cones-T1), adiabatic T1ρ (Cones-AdiabT1ρ), T2* (Cones-T2*), and magnetization transfer (Cones-MT) sequences at 3 T. Manual segmentation was performed by two experienced radiologists, and automatic segmentation was completed using the proposed U-Net CNN model. The accuracy of cartilage segmentation was evaluated using the Dice score and volumetric overlap error (VOE). Pearson correlation coefficient and intraclass correlation coefficient (ICC) were calculated to evaluate the consistency of quantitative MR parameters extracted from automatic and manual segmentations. UTE biomarkers were compared among different subject groups using one-way ANOVA. RESULTS The U-Net CNN model provided reliable cartilage segmentation with a mean Dice score of 0.82 and a mean VOE of 29.86%. The consistency of Cones-T1, Cones-AdiabT1ρ, Cones-T2*, and MMF calculated using automatic and manual segmentations ranged from 0.91 to 0.99 for Pearson correlation coefficients, and from 0.91 to 0.96 for ICCs, respectively. Significant increases in Cones-T1, Cones-AdiabT1ρ, and Cones-T2* (p < 0.05) and a decrease in MMF (p < 0.001) were observed in doubtful-minimal OA and/or moderate-severe OA over normal controls. CONCLUSION Quantitative 3D UTE cones MR imaging combined with the proposed U-Net CNN model allows a fully automated comprehensive assessment of articular cartilage. KEY POINTS • 3D UTE cones imaging combined with U-Net CNN model was able to provide fully automated cartilage segmentation. • UTE parameters obtained from automatic segmentation were able to reliably provide a quantitative assessment of cartilage.
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Yong CW, Lai KW, Murphy BP, Hum YC. Comparative Study of Encoder-decoder-based Convolutional Neural Networks in Cartilage Delineation from Knee Magnetic Resonance Images. Curr Med Imaging 2021; 17:981-987. [PMID: 33319690 PMCID: PMC8653427 DOI: 10.2174/1573405616666201214122409] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 09/23/2020] [Accepted: 10/14/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Osteoarthritis (OA) is a common degenerative joint inflammation that may lead to disability. Although OA is not lethal, this disease will remarkably affect patient's mobility and their daily lives. Detecting OA at an early stage allows for early intervention and may slow down disease progression. INTRODUCTION Magnetic resonance imaging is a useful technique to visualize soft tissues within the knee joint. Cartilage delineation in magnetic resonance (MR) images helps in understanding the disease progressions. Convolutional neural networks (CNNs) have shown promising results in computer vision tasks, and various encoder-decoder-based segmentation neural networks are introduced in the last few years. However, the performances of such networks are unknown in the context of cartilage delineation. METHODS This study trained and compared 10 encoder-decoder-based CNNs in performing cartilage delineation from knee MR images. The knee MR images are obtained from the Osteoarthritis Initiative (OAI). The benchmarking process is to compare various CNNs based on physical specifications and segmentation performances. RESULTS LadderNet has the least trainable parameters with the model size of 5 MB. UNetVanilla crowned the best performances by having 0.8369, 0.9108, and 0.9097 on JSC, DSC, and MCC. CONCLUSION UNetVanilla can be served as a benchmark for cartilage delineation in knee MR images, while LadderNet served as an alternative if there are hardware limitations during production.
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Affiliation(s)
- Ching Wai Yong
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 1900 Kampar, Perak, Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 1900 Kampar, Perak, Kuala Lumpur, Malaysia
| | - Belinda Pingguan Murphy
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 1900 Kampar, Perak, Kuala Lumpur, Malaysia
| | - Yan Chai Hum
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 1900 Kampar, Perak, Kuala Lumpur, Malaysia
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From classical to deep learning: review on cartilage and bone segmentation techniques in knee osteoarthritis research. Artif Intell Rev 2020. [DOI: 10.1007/s10462-020-09924-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Bonaretti S, Gold GE, Beaupre GS. pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage. PLoS One 2020; 15:e0226501. [PMID: 31978052 PMCID: PMC6980400 DOI: 10.1371/journal.pone.0226501] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 11/27/2019] [Indexed: 02/04/2023] Open
Abstract
Transparent research in musculoskeletal imaging is fundamental to reliably investigate diseases such as knee osteoarthritis (OA), a chronic disease impairing femoral knee cartilage. To study cartilage degeneration, researchers have developed algorithms to segment femoral knee cartilage from magnetic resonance (MR) images and to measure cartilage morphology and relaxometry. The majority of these algorithms are not publicly available or require advanced programming skills to be compiled and run. However, to accelerate discoveries and findings, it is crucial to have open and reproducible workflows. We present pyKNEEr, a framework for open and reproducible research on femoral knee cartilage from MR images. pyKNEEr is written in python, uses Jupyter notebook as a user interface, and is available on GitHub with a GNU GPLv3 license. It is composed of three modules: 1) image preprocessing to standardize spatial and intensity characteristics; 2) femoral knee cartilage segmentation for intersubject, multimodal, and longitudinal acquisitions; and 3) analysis of cartilage morphology and relaxometry. Each module contains one or more Jupyter notebooks with narrative, code, visualizations, and dependencies to reproduce computational environments. pyKNEEr facilitates transparent image-based research of femoral knee cartilage because of its ease of installation and use, and its versatility for publication and sharing among researchers. Finally, due to its modular structure, pyKNEEr favors code extension and algorithm comparison. We tested our reproducible workflows with experiments that also constitute an example of transparent research with pyKNEEr, and we compared pyKNEEr performances to existing algorithms in literature review visualizations. We provide links to executed notebooks and executable environments for immediate reproducibility of our findings.
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Affiliation(s)
- Serena Bonaretti
- Department of Radiology, Stanford University, Stanford, CA, United States of America
- Musculoskeletal Research Laboratory, VA Palo Alto Health Care System, Palo Alto, CA, United States of America
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, CA, United States of America
| | - Gary S. Beaupre
- Musculoskeletal Research Laboratory, VA Palo Alto Health Care System, Palo Alto, CA, United States of America
- Department of Bioengineering, Stanford University, Stanford, CA, United States of America
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Thaha R, Jogi SP, Rajan S, Mahajan V, Venugopal VK, Mehndiratta A, Singh A. Modified radial-search algorithm for segmentation of tibiofemoral cartilage in MR images of patients with subchondral lesion. Int J Comput Assist Radiol Surg 2020; 15:403-413. [DOI: 10.1007/s11548-020-02116-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 01/06/2020] [Indexed: 02/06/2023]
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Hesper T, Bittersohl B, Schleich C, Hosalkar H, Krauspe R, Krekel P, Zilkens C. Automatic Cartilage Segmentation for Delayed Gadolinium-Enhanced Magnetic Resonance Imaging of Hip Joint Cartilage: A Feasibility Study. Cartilage 2020; 11:32-37. [PMID: 29926743 PMCID: PMC6921955 DOI: 10.1177/1947603518783481] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
OBJECTIVE Automatic segmentation for biochemical cartilage evaluation holds promise for an efficient and reader-independent analysis. This pilot study aims to investigate the feasibility and to compare delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC) hip joint assessment with manual segmentation of acetabular and femoral head cartilage and dGEMRIC hip joint assessment using automatic surface and volume processing software at 3 Tesla. DESIGN Three-dimensional (3D) dGEMRIC data sets of 6 patients with hip-related pathology were assessed (1) manually including multiplanar image reformatting and regions of interest (ROI) analysis and (2) automated by using a combined surface and volume processing software. For both techniques, T1Gd values were obtained in acetabular and femoral head cartilage at 7 regions (anterior, anterior-superior, superior-anterior, superior, superior-posterior, posterior-superior, and posterior) in central and peripheral portions. Correlation between both techniques was calculated utilizing Spearman's rank correlation coefficient. RESULTS A high correlation between both techniques was observed for acetabular (ρ = 0.897; P < 0.001) and femoral head (ρ = 0.894; P < 0.001) cartilage in all analyzed regions of the hip joint (ρ between 0.755 and 0.955; P < 0.001). CONCLUSIONS Automatic cartilage segmentation with dGEMRIC assessment for hip joint cartilage evaluation seems feasible providing high to excellent correlation with manually performed ROI analysis. This technique is feasible for an objective, reader-independant and reliable assessment of biochemical cartilage status.
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Affiliation(s)
- Tobias Hesper
- Department of Orthopedics, Medical
Faculty, University of Düsseldorf, Düsseldorf, Germany
| | - Bernd Bittersohl
- Department of Orthopedics, Medical
Faculty, University of Düsseldorf, Düsseldorf, Germany,Bernd Bittersohl, Department of Orthopedics,
Heinrich-Heine University, Düsseldorf, Moorenstr. 5, 40225 Düsseldorf, Germany.
| | - Christoph Schleich
- Department of Diagnostic and
Interventional Radiology, Medical Faculty, University of Düsseldorf, Düsseldorf,
Germany
| | - Harish Hosalkar
- Paradise Valley Hospital, San Diego, CA,
USA,Tri-city Medical Center, San Diego, CA,
USA
| | - Rüdiger Krauspe
- Department of Orthopedics, Medical
Faculty, University of Düsseldorf, Düsseldorf, Germany
| | | | - Christoph Zilkens
- Department of Orthopedics, Medical
Faculty, University of Düsseldorf, Düsseldorf, Germany
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Lim M, Hacihaliloglu I. Structure-enhanced local phase filtering using L0 gradient minimization for efficient semiautomated knee magnetic resonance imaging segmentation. J Med Imaging (Bellingham) 2016; 3:044503. [PMID: 27981068 DOI: 10.1117/1.jmi.3.4.044503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Accepted: 11/04/2016] [Indexed: 11/14/2022] Open
Abstract
The segmentation of bone surfaces from magnetic resonance imaging (MRI) data has applications in the quantitative measurement of knee osteoarthritis, surgery planning for patient-specific total knee arthroplasty, and its subsequent fabrication of artificial implants. However, due to the problems associated with MRI imaging, such as low contrast between bone and surrounding tissues, noise, bias fields, and the partial volume effect, segmentation of bone surfaces continues to be a challenging operation. A framework is presented for the enhancement of knee MRI scans prior to segmentation in order to obtain high contrast bone images. During the first stage, a contrast enhanced relative total variation regularization method is used in order to remove textural noise from the bone structures and surrounding soft tissue interface. This salient bone edge information is further enhanced using a sparse gradient counting method based on [Formula: see text] gradient minimization, which globally controls how many nonzero gradients are resulted in order to approximate prominent bone structures in a structure-sparsity-management manner. The last stage of the framework involves incorporation of local phase bone boundary information in order to provide an intensity invariant enhancement of contrast between the bone and surrounding soft tissue. The enhanced images are segmented using a fast random walker algorithm. Validation against expert segmentation was performed on 20 clinical knee MRI volumes and achieved a mean dice similarity coefficient of 0.949.
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Affiliation(s)
- Mikhiel Lim
- Rutgers, The State University , Department of Biomedical Engineering, 599 Taylor Road, Piscataway, New Jersey 08854, United States
| | - Ilker Hacihaliloglu
- Rutgers, The State University , Department of Biomedical Engineering, 599 Taylor Road, Piscataway, New Jersey 08854, United States
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Pedoia V, Majumdar S, Link TM. Segmentation of joint and musculoskeletal tissue in the study of arthritis. MAGMA (NEW YORK, N.Y.) 2016; 29:207-21. [PMID: 26915082 PMCID: PMC7181410 DOI: 10.1007/s10334-016-0532-9] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2015] [Revised: 02/05/2016] [Accepted: 02/08/2016] [Indexed: 12/26/2022]
Abstract
As the most frequent cause of physical disability, musculoskeletal diseases such as arthritis and osteoporosis have a great social and economical impact. Quantitative magnetic resonance imaging (MRI) biomarkers are important tools that allow clinicians to better characterize, monitor, and even predict musculoskeletal disease progression. Post-processing pipelines often include image segmentation. Manually identifying the border of the region of interest (ROI) is a difficult and time-consuming task. Manual segmentation is also affected by inter- and intrauser variability, thus limiting standardization. Fully automatic or semi-automatic methods that minimize the user interaction are highly desirable. Unfortunately, an ultimate, highly reliable and extensively evaluated solution for joint and musculoskeletal tissue segmentation has not yet been proposed, and many clinical studies still adopt fully manual procedures. Moreover, the clinical translation of several promising quantitative MRI techniques is highly affected by the lack of an established, fast, and accurate segmentation method. The goal of this review is to present some of the techniques proposed in recent literature that have been adopted in clinical studies for joint and musculoskeletal tissue analyses in arthritis patients. The most widely used MRI sequences and image processing algorithms employed to accomplish segmentation challenges will be discussed in this paper.
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Affiliation(s)
- Valentina Pedoia
- Musculoskeletal Quantitative Imaging Research Group, Department of Radiology and Biomedical Imaging, UC San Francisco, 1700 Fourth Street, Suite 201, QB3 Building, San Francisco, CA, 94107, USA.
| | - Sharmila Majumdar
- Musculoskeletal Quantitative Imaging Research Group, Department of Radiology and Biomedical Imaging, UC San Francisco, 1700 Fourth Street, Suite 201, QB3 Building, San Francisco, CA, 94107, USA
| | - Thomas M Link
- Musculoskeletal Quantitative Imaging Research Group, Department of Radiology and Biomedical Imaging, UC San Francisco, 1700 Fourth Street, Suite 201, QB3 Building, San Francisco, CA, 94107, USA
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Ramme AJ, Guss MS, Vira S, Vigdorchik JM, Newe A, Raithel E, Chang G. Evaluation of Automated Volumetric Cartilage Quantification for Hip Preservation Surgery. J Arthroplasty 2016; 31:64-9. [PMID: 26377376 DOI: 10.1016/j.arth.2015.08.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Revised: 07/24/2015] [Accepted: 08/10/2015] [Indexed: 02/01/2023] Open
Abstract
Automating the process of femoroacetabular cartilage identification from magnetic resonance imaging (MRI) images has important implications to guiding clinical care by providing a temporal metric that allows for optimizing the timing for joint preservation surgery. In this paper, we evaluate a new automated cartilage segmentation method using a time trial, segmented volume comparison, overlap metrics, and Euclidean distance mapping. We report interrater overlap metrics using the true fast imaging with steady-state precession MRI sequence of 0.874, 0.546, and 0.704 for the total overlap, union overlap, and mean overlap, respectively. This method was 3.28× faster than manual segmentation. This technique provides clinicians with volumetric cartilage information that is useful for optimizing the timing for joint preservation procedures.
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Affiliation(s)
- Austin J Ramme
- Department of Orthopaedic Surgery, New York University Hospital for Joint Diseases, New York, New York
| | - Michael S Guss
- Department of Orthopaedic Surgery, New York University Hospital for Joint Diseases, New York, New York
| | - Shaleen Vira
- Department of Orthopaedic Surgery, New York University Hospital for Joint Diseases, New York, New York
| | - Jonathan M Vigdorchik
- Department of Orthopaedic Surgery, New York University Hospital for Joint Diseases, New York, New York
| | - Axel Newe
- Methodpark Engineering GmbH, Erlangen, Germany; Chair of Medical Informatics, Friedrich-Alexander University, Erlangen-Nuremberg, Erlangen, Germany
| | | | - Gregory Chang
- Department of Radiology, Center for Musculoskeletal Care, NYU Langone Medical Center, New York, New York
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