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Slovák L, Panfilov E, Zahradník D, Casula V, Nieminen MT, Land WM, Iwatsuki T, Abdollahipour R. External Focus of Attention Reduces Cartilage Load During Drop Landings. Scand J Med Sci Sports 2024; 34:e14718. [PMID: 39215390 DOI: 10.1111/sms.14718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 08/12/2024] [Accepted: 08/13/2024] [Indexed: 09/04/2024]
Abstract
The aim of the present study was to examine the effects of attentional focus instructions on acute changes in the transverse relaxation time (T2) of the femorotibial cartilage and in cartilage volume during repeated drop-jump landings. Ten healthy females (Mage = 20.4 ± 0.8 years) performed a drop landing task from a 50 cm high box over the course of 3 days (50 repetitions each day) across three attentional focus conditions: external focus (EF: focus on landing as soft as possible), internal focus (IF: focus on bending your knees when you land), and control (CON: no-focus instruction), which was counterbalanced across focus conditions. T2 mapping and the volume of femorotibial cartilage were determined from magnetic resonance imaging scans at 1.5 T for the dominant knee before and after completing the drop landings in each attentional focus condition per day. Results indicated a smaller change in cartilage T2 relaxation time and volumetry in the central load-bearing lateral cartilage under the EF, compared to IF and CON. Moreover, the change in T2 and cartilage volume was greater for lateral tibial cartilage as compared to femoral cartilage and was independent of attentional focus instructions. No significant acute quantitative changes were observed in the medial compartment. The peak vertical ground reaction force was found to be the lowest under the EF, compared to IF and CON. These findings suggest that external focus of attention may reduce cartilage load, potentially aiding in the control or management of cartilage injuries during landing in female athletes.
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Affiliation(s)
- Lukáš Slovák
- Human Motion Diagnostic Centre, University of Ostrava, Ostrava, Czech Republic
| | - Egor Panfilov
- Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
| | - David Zahradník
- Human Motion Diagnostic Centre, University of Ostrava, Ostrava, Czech Republic
| | - Victor Casula
- Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
| | - Miika T Nieminen
- Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
- Department of Diagnostics, Oulu University Hospital, Oulu, Finland
| | - William M Land
- Department of Kinesiology, College for Health, Community and Policy, The University of Texas at San Antonio, San Antonio, USA
| | - Takehiro Iwatsuki
- Department of Kinesiology and Exercise Sciences, University of Hawaii at Hilo, Hilo, Hawaii, USA
| | - Reza Abdollahipour
- Department of Natural Sciences in Kinanthropology, Faculty of Physical Culture, Palacky University Olomouc, Olomouc, Czech Republic
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Surendran T, Park LK, Lauber MV, Cha B, Jhun RS, Capellini TD, Kumar D, Felson DT, Kolachalama VB. Survival analysis on subchondral bone length for total knee replacement. Skeletal Radiol 2024; 53:1541-1552. [PMID: 38388702 PMCID: PMC11194148 DOI: 10.1007/s00256-024-04627-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 02/01/2024] [Accepted: 02/15/2024] [Indexed: 02/24/2024]
Abstract
OBJECTIVE Use subchondral bone length (SBL), a new MRI-derived measure that reflects the extent of cartilage loss and bone flattening, to predict the risk of progression to total knee replacement (TKR). METHODS We employed baseline MRI data from the Osteoarthritis Initiative (OAI), focusing on 760 men and 1214 women with bone marrow lesions (BMLs) and joint space narrowing (JSN) scores, to predict the progression to TKR. To minimize bias from analyzing both knees of a participant, only the knee with a higher Kellgren-Lawrence (KL) grade was considered, given its greater potential need for TKR. We utilized the Kaplan-Meier survival curves and Cox proportional hazards models, incorporating raw and normalized values of SBL, JSN, and BML as predictors. The study included subgroup analyses for different demographics and clinical characteristics, using models for raw and normalized SBL (merged, femoral, tibial), BML (merged, femoral, tibial), and JSN (medial and lateral compartments). Model performance was evaluated using the time-dependent area under the curve (AUC), Brier score, and Concordance index to gauge accuracy, calibration, and discriminatory power. Knee joint and region-level analyses were conducted to determine the effectiveness of SBL, JSN, and BML in predicting TKR risk. RESULTS The SBL model, incorporating data from both the femur and tibia, demonstrated a predictive capacity for TKR that closely matched the performance of the BML score and the JSN grade. The Concordance index of the SBL model was 0.764, closely mirroring the BML's 0.759 and slightly below JSN's 0.788. The Brier score for the SBL model stood at 0.069, showing comparability with BML's 0.073 and a minor difference from JSN's 0.067. Regarding the AUC, the SBL model achieved 0.803, nearly identical to BML's 0.802 and slightly lower than JSN's 0.827. CONCLUSION SBL's capacity to predict the risk of progression to TKR highlights its potential as an effective imaging biomarker for knee osteoarthritis.
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Affiliation(s)
- Tejus Surendran
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA
| | - Lisa K Park
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA
| | - Meagan V Lauber
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA
| | - Baekdong Cha
- Sargent College, Boston University, Boston, MA, USA
| | - Ray S Jhun
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA
| | - Terence D Capellini
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Deepak Kumar
- Sargent College, Boston University, Boston, MA, USA
| | - David T Felson
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA
| | - Vijaya B Kolachalama
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, 72 E. Concord Street, Evans 636, Boston, MA, 02118, USA.
- Department of Computer Science and Faculty of Computing & Data Sciences, Boston University, Boston, MA, 02215, USA.
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Champagne AA, Zuleger TM, Warren SM, Smith DR, Lamplot JD, Xerogeanes JW, Slutsky-Ganesh AB, Jayaram P, Patel JM, Myer GD, Diekfuss JA. Automated quantitative assessment of bone contusions and overlying articular cartilage following anterior cruciate ligament injury. J Orthop Res 2024. [PMID: 38885494 DOI: 10.1002/jor.25920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/15/2024] [Accepted: 06/01/2024] [Indexed: 06/20/2024]
Abstract
Quantitative methods to characterize bone contusions and associated cartilage injury remain limited. We combined standardized voxelwise normalization and 3D mapping to automate bone contusion segmentation post-anterior cruciate ligament (ACL) injury and evaluate anomalies in articular cartilage overlying bone contusions. Forty-five patients (54% female, 26.4 ± 11.8 days post-injury) with an ACL tear underwent 3T magnetic resonance imaging of their involved and uninvolved knees. A novel method for voxelwise normalization and 3D anatomical mapping was used to automate segmentation, labeling, and localization of bone contusions in the involved knee. The same mapping system was used to identify the associated articular cartilage overlying bone lesions. Mean regional T1ρ was extracted from articular cartilage regions in both the involved and uninvolved knees for quantitative paired analysis against ipsilateral cartilage within the same compartment outside of the localized bone contusion. At least one bone contusion lesion was detected in the involved knee within the femur and/or tibia following ACL injury in 42 participants. Elevated T1ρ (p = 0.033) signal were documented within the articular cartilage overlying the bone contusions resulting from ACL injury. In contrast, the same cartilaginous regions deprojected onto the uninvolved knees showed no ipsilateral differences (p = 0.795). Automated bone contusion segmentation using standardized voxelwise normalization and 3D mapping deprojection identified altered cartilage overlying bone contusions in the setting of knee ACL injury.
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Affiliation(s)
- Allen A Champagne
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, North Carolina, USA
| | - Taylor M Zuleger
- Emory Sports Performance And Research Center (SPARC), Flowery Branch, Georgia, USA
- Emory Sports Medicine Center, Atlanta, Georgia, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia, USA
- Neuroscience Graduate Program, University of Cincinnati, Cincinnati, Ohio, USA
| | - Shayla M Warren
- Emory Sports Performance And Research Center (SPARC), Flowery Branch, Georgia, USA
- Emory Sports Medicine Center, Atlanta, Georgia, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Daniel R Smith
- Emory Sports Performance And Research Center (SPARC), Flowery Branch, Georgia, USA
- Emory Sports Medicine Center, Atlanta, Georgia, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia, USA
| | | | - John W Xerogeanes
- Emory Sports Medicine Center, Atlanta, Georgia, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Alexis B Slutsky-Ganesh
- Emory Sports Performance And Research Center (SPARC), Flowery Branch, Georgia, USA
- Emory Sports Medicine Center, Atlanta, Georgia, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia, USA
- Department of Kinesiology, University of North Carolina at Greensboro, Greensboro, North Carolina, USA
| | - Prathap Jayaram
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Jay M Patel
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Gregory D Myer
- Emory Sports Performance And Research Center (SPARC), Flowery Branch, Georgia, USA
- Emory Sports Medicine Center, Atlanta, Georgia, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia, USA
- The Micheli Center for Sports Injury Prevention, Waltham, Massachusetts, USA
- Youth Physical Development Center, Cardiff Metropolitan University, Wales, UK
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, Georgia, United States
| | - Jed A Diekfuss
- Emory Sports Performance And Research Center (SPARC), Flowery Branch, Georgia, USA
- Emory Sports Medicine Center, Atlanta, Georgia, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, Georgia, USA
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Berrimi M, Hans D, Jennane R. A semi-supervised multiview-MRI network for the detection of Knee Osteoarthritis. Comput Med Imaging Graph 2024; 114:102371. [PMID: 38513397 DOI: 10.1016/j.compmedimag.2024.102371] [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: 09/28/2023] [Revised: 03/09/2024] [Accepted: 03/13/2024] [Indexed: 03/23/2024]
Abstract
Knee OsteoArthritis (OA) is a prevalent chronic condition, affecting a significant proportion of the global population. Detecting knee OA is crucial as the degeneration of the knee joint is irreversible. In this paper, we introduce a semi-supervised multi-view framework and a 3D CNN model for detecting knee OA using 3D Magnetic Resonance Imaging (MRI) scans. We introduce a semi-supervised learning approach combining labeled and unlabeled data to improve the performance and generalizability of the proposed model. Experimental results show the efficacy of our proposed approach in detecting knee OA from 3D MRI scans using a large cohort of 4297 subjects. An ablation study was conducted to investigate the contributions of various components of the proposed model, providing insights into the optimal design of the model. Our results indicate the potential of the proposed approach to improve the accuracy and efficiency of OA diagnosis. The proposed framework reported an AUC of 93.20% for the detection of knee OA.
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Affiliation(s)
- Mohamed Berrimi
- University of Orleans, Institut Denis Poisson, UMR CNRS 7013, Orleans, 45067, France
| | - Didier Hans
- Lausanne University Hospital, Center of Bone Diseases & University of Lausanne, Lausanne, Switzerland
| | - Rachid Jennane
- University of Orleans, Institut Denis Poisson, UMR CNRS 7013, Orleans, 45067, France.
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Stoel BC, Staring M, Reijnierse M, van der Helm-van Mil AHM. Deep learning in rheumatological image interpretation. Nat Rev Rheumatol 2024; 20:182-195. [PMID: 38332242 DOI: 10.1038/s41584-023-01074-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/20/2023] [Indexed: 02/10/2024]
Abstract
Artificial intelligence techniques, specifically deep learning, have already affected daily life in a wide range of areas. Likewise, initial applications have been explored in rheumatology. Deep learning might not easily surpass the accuracy of classic techniques when performing classification or regression on low-dimensional numerical data. With images as input, however, deep learning has become so successful that it has already outperformed the majority of conventional image-processing techniques developed during the past 50 years. As with any new imaging technology, rheumatologists and radiologists need to consider adapting their arsenal of diagnostic, prognostic and monitoring tools, and even their clinical role and collaborations. This adaptation requires a basic understanding of the technical background of deep learning, to efficiently utilize its benefits but also to recognize its drawbacks and pitfalls, as blindly relying on deep learning might be at odds with its capabilities. To facilitate such an understanding, it is necessary to provide an overview of deep-learning techniques for automatic image analysis in detecting, quantifying, predicting and monitoring rheumatic diseases, and of currently published deep-learning applications in radiological imaging for rheumatology, with critical assessment of possible limitations, errors and confounders, and conceivable consequences for rheumatologists and radiologists in clinical practice.
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Affiliation(s)
- Berend C Stoel
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Marius Staring
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Monique Reijnierse
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
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Daneshmand M, Panfilov E, Bayramoglu N, Korhonen RK, Saarakkala S. Deep learning based detection of osteophytes in radiographs and magnetic resonance imagings of the knee using 2D and 3D morphology. J Orthop Res 2024. [PMID: 38323840 DOI: 10.1002/jor.25800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 10/26/2023] [Accepted: 01/19/2024] [Indexed: 02/08/2024]
Abstract
In this study, we investigated the discriminative capacity of knee morphology in automatic detection of osteophytes defined by the Osteoarthritis Research Society International atlas, using X-ray and magnetic resonance imaging (MRI) data. For the X-ray analysis, we developed a deep learning (DL) based model to segment femur and tibia. In case of MRIs, we utilized previously validated segmentations of femur, tibia, corresponding cartilage tissues, and menisci. Osteophyte detection was performed using DL models in four compartments: medial femur (FM), lateral femur (FL), medial tibia (TM), and lateral tibia (TL). To analyze the confounding effects of soft tissues, we investigated their morphology in combination with bones, including bones+cartilage, bones+menisci, and all the tissues. From X-ray-based 2D morphology, the models yielded balanced accuracy of 0.73, 0.69, 0.74, and 0.74 for FM, FL, TM, TL, respectively. Using 3D bone morphology from MRI, balanced accuracy was 0.80, 0.77, 0.71, and 0.76, respectively. The performance was higher than in 2D for all the compartments except for TM, with significant improvements observed for femoral compartments. Adding menisci or cartilage morphology consistently improved balanced accuracy in TM, with the greatest improvement seen for small osteophyte. Otherwise, the models performed similarly to bones-only. Our experiments demonstrated that MRI-based models show higher detection capability than X-ray based models for identifying knee osteophytes. This study highlighted the feasibility of automated osteophyte detection from X-ray and MRI data and suggested further need for development of osteophyte assessment criteria in addition to OARSI, particularly, for early osteophytic changes.
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Affiliation(s)
| | - Egor Panfilov
- Faculty of Medicine, University of Oulu, Oulu, Finland
| | | | | | - Simo Saarakkala
- University of Oulu and Oulu University Hospital, Oulu, Finland
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7
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Zhang R, Zhou X, Raithel E, Ren C, Zhang P, Li J, Bai L, Zhao J. A reproducibility study of knee cartilage volume and thickness values derived by fully automatic segmentation based on three-dimensional dual-echo in steady state data from 1.5 T and 3 T magnetic resonance imaging. MAGMA (NEW YORK, N.Y.) 2024; 37:69-82. [PMID: 37815638 DOI: 10.1007/s10334-023-01122-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 09/05/2023] [Accepted: 09/13/2023] [Indexed: 10/11/2023]
Abstract
OBJECTIVE To evaluate the repeatability of cartilage volume and thickness values at 1.5 T MRI using a fully automatic cartilage segmentation method and reproducibility of the method between 1.5 T and 3 T data. METHODS The study included 20 knee joints from 10 healthy subjects with each subject having undergone double-knee MRI. All knees were scanned at 1.5 T and 3 T MR scanners using a three-dimensional (3D) high-resolution dual-echo in steady state (DESS) sequence. Cartilage volume and thickness of 21 subregions were quantified using a fully automatic cartilage segmentation research application (MR Chondral Health, version 3.0, Siemens Healthcare, Erlangen, Germany). The volume and thickness values derived from fully automatically computed segmentation masks were analyzed for the scan-rescan data from the same volunteers. The accuracy of the automatic segmentation of the cartilage in 1.5 T images was evaluated by the dice similarity coefficient (DSC) and Hausdorff distance (HD) using the manually corrected segmentation as a reference. The volume and thickness values calculated from 1.5 T and 3 T were also compared. RESULTS No statistically significant differences were found for cartilage thickness or volume across all subregions between the scan-rescanned data at 1.5 T (P > 0.05). The mean DSC between the fully automatic and manually corrected knee cartilage segmentation contours at 1.5 T was 0.9946. The average value of HD was 2.41 mm. Overall, there was no statistically significant difference in the cartilage volume or thickness in most-subregions between the two field strengths (P > 0.05) except for the medial region of femur and tibia. Bland-Altman plot and intraclass correlation coefficient (ICC) showed high consistency between results obtained based on same and different scanning sequences. CONCLUSION The cartilage segmentation software had high repeatability for DESS images obtained from the same device. In addition, the overall reproducibility of the images obtained from equipment of two different field strengths was satisfactory.
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Affiliation(s)
- Ranxu Zhang
- Department of CT/MR, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, 050051, China
| | - Xiaoyue Zhou
- MR Collaboration, Siemens Healthineers Ltd, Shanghai, 200126, China
| | | | - Congcong Ren
- Department of CT/MR, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, 050051, China
| | - Ping Zhang
- Department of CT/MR, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, 050051, China
| | - Junfei Li
- Department of CT/MR, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, 050051, China
| | - Lin Bai
- Department of CT/MR, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, 050051, China
| | - Jian Zhao
- Department of CT/MR, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, 050051, China.
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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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Jarraya M, Guermazi A, Roemer FW. Osteoarthritis year in review 2023: Imaging. Osteoarthritis Cartilage 2024; 32:18-27. [PMID: 37879600 DOI: 10.1016/j.joca.2023.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 09/24/2023] [Accepted: 10/17/2023] [Indexed: 10/27/2023]
Abstract
PURPOSE This narrative review summarizes the original research in the field of in vivo osteoarthritis (OA) imaging between 1 January 2022 and 1 April 2023. METHODS A PubMed search was conducted using the following several terms pertaining to OA imaging, including but not limited to "Osteoarthritis / OA", "Magnetic resonance imaging / MRI", "X-ray" "Computed tomography / CT", "artificial intelligence /AI", "deep learning", "machine learning". This review is organized by topics including the anatomical structure of interest and modality, AI, challenges of OA imaging in the context of clinical trials, and imaging biomarkers in clinical trials and interventional studies. Ex vivo and animal studies were excluded from this review. RESULTS Two hundred and forty-nine publications were relevant to in vivo human OA imaging. Among the articles included, the knee joint (61%) and MRI (42%) were the predominant anatomical area and imaging modalities studied. Marked heterogeneity of structural tissue damage in OA knees was reported, a finding of potential relevance to clinical trial inclusion. The use of AI continues to rise rapidly to be applied in various aspect of OA imaging research but a lack of generalizability beyond highly standardized datasets limit interpretation and wide-spread application. No pharmacologic clinical trials using imaging data as outcome measures have been published in the period of interest. CONCLUSIONS Recent advances in OA imaging continue to heavily weigh on the use of AI. MRI remains the most important modality with a growing role in outcome prediction and classification.
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Affiliation(s)
- Mohamed Jarraya
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Ali Guermazi
- Department of Radiology, VA Boston Healthcare System, Boston University School of Medicine, Boston, MA, USA.
| | - Frank W Roemer
- Department of Radiology, VA Boston Healthcare System, Boston University School of Medicine, Boston, MA, USA; Department of Radiology, Universitätsklinikum Erlangen & Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
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10
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Yao Y, Zhong J, Zhang L, Khan S, Chen W. CartiMorph: A framework for automated knee articular cartilage morphometrics. Med Image Anal 2024; 91:103035. [PMID: 37992496 DOI: 10.1016/j.media.2023.103035] [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: 10/13/2022] [Revised: 08/25/2023] [Accepted: 11/13/2023] [Indexed: 11/24/2023]
Abstract
We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient ρ∈[0.82,0.97]), surface area (ρ∈[0.82,0.98]) and volume (ρ∈[0.89,0.98]) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.
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Affiliation(s)
- Yongcheng Yao
- CU Lab of AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China.
| | - Junru Zhong
- CU Lab of AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China
| | - Liping Zhang
- CU Lab of AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China
| | - Sheheryar Khan
- School of Professional Education and Executive Development, The Hong Kong Polytechnic University, Hong Kong, China
| | - Weitian Chen
- CU Lab of AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China.
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11
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Kijowski R, Fritz J, Deniz CM. Deep learning applications in osteoarthritis imaging. Skeletal Radiol 2023; 52:2225-2238. [PMID: 36759367 PMCID: PMC10409879 DOI: 10.1007/s00256-023-04296-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/22/2022] [Accepted: 01/31/2023] [Indexed: 02/11/2023]
Abstract
Deep learning (DL) is one of the most exciting new areas in medical imaging. This article will provide a review of current applications of DL in osteoarthritis (OA) imaging, including methods used for cartilage lesion detection, OA diagnosis, cartilage segmentation, and OA risk assessment. DL techniques have been shown to have similar diagnostic performance as human readers for detecting and grading cartilage lesions within the knee on MRI. A variety of DL methods have been developed for detecting and grading the severity of knee OA and various features of knee OA on X-rays using standardized classification systems with diagnostic performance similar to human readers. Multiple DL approaches have been described for fully automated segmentation of cartilage and other knee tissues and have achieved higher segmentation accuracy than currently used methods with substantial reductions in segmentation times. Various DL models analyzing baseline X-rays and MRI have been developed for OA risk assessment. These models have shown high diagnostic performance for predicting a wide variety of OA outcomes, including the incidence and progression of radiographic knee OA, the presence and progression of knee pain, and future total knee replacement. The preliminary results of DL applications in OA imaging have been encouraging. However, many DL techniques require further technical refinement to maximize diagnostic performance. Furthermore, the generalizability of DL approaches needs to be further investigated in prospective studies using large image datasets acquired at different institutions with different imaging hardware before they can be implemented in clinical practice and research studies.
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Affiliation(s)
- Richard Kijowski
- Department of Radiology, New York University Grossman School of Medicine, 660 First Avenue, 3Rd Floor, New York, NY, 10016, USA.
| | - Jan Fritz
- Department of Radiology, New York University Grossman School of Medicine, 660 First Avenue, 3Rd Floor, New York, NY, 10016, USA
| | - Cem M Deniz
- Department of Radiology, New York University Grossman School of Medicine, 660 First Avenue, 3Rd Floor, New York, NY, 10016, USA
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12
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Gao KT, Xie E, Chen V, Iriondo C, Calivà F, Souza RB, Majumdar S, Pedoia V. Large-Scale Analysis of Meniscus Morphology as Risk Factor for Knee Osteoarthritis. Arthritis Rheumatol 2023; 75:1958-1968. [PMID: 37262347 DOI: 10.1002/art.42623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 03/24/2023] [Accepted: 05/25/2023] [Indexed: 06/03/2023]
Abstract
OBJECTIVE Although it is established that structural damage of the meniscus is linked to knee osteoarthritis (OA) progression, the predisposition to future development of OA because of geometric meniscal shapes is plausible and unexplored. This study aims to identify common variations in meniscal shape and determine their relationships to tissue morphology, OA onset, and longitudinal changes in cartilage thickness. METHODS A total of 4,790 participants from the Osteoarthritis Initiative data set were studied. A statistical shape model was developed for the meniscus, and shape scores were evaluated between a control group and an OA incidence group. Shape features were then associated with cartilage thickness changes over 8 years to localize the relationship between meniscus shape and cartilage degeneration. RESULTS Seven shape features between the medial and lateral menisci were identified to be different between knees that remain normal and those that develop OA. These include length-width ratios, horn lengths, root attachment angles, and concavity. These "at-risk" shapes were linked to unique cartilage thickness changes that suggest a relationship between meniscus geometry and decreased tibial coverage and rotational imbalances. Additionally, strong associations were found between meniscal shape and demographic subpopulations, future tibial extrusion, and meniscal and ligamentous tears. CONCLUSION This automatic method expanded upon known meniscus characteristics that are associated with the onset of OA and discovered novel shape features that have yet to be investigated in the context of OA risk.
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Affiliation(s)
- Kenneth T Gao
- University of California, San Francisco and University of California Berkeley-University of California San Francisco Graduate Program in Bioengineering, San Francisco, United States
| | - Emily Xie
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
| | - Vincent Chen
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
| | - Claudia Iriondo
- University of California, San Francisco and University of California Berkeley-University of California San Francisco Graduate Program in Bioengineering, San Francisco, United States
| | - Francesco Calivà
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
| | - Richard B Souza
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco and Department of Physical Therapy and Rehabilitation Science, University of California, San Francisco, United States
| | - Sharmila Majumdar
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
| | - Valentina Pedoia
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
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13
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Khader A, Alquran H. Automated Prediction of Osteoarthritis Level in Human Osteochondral Tissue Using Histopathological Images. Bioengineering (Basel) 2023; 10:764. [PMID: 37508791 PMCID: PMC10376879 DOI: 10.3390/bioengineering10070764] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/21/2023] [Accepted: 06/23/2023] [Indexed: 07/30/2023] Open
Abstract
Osteoarthritis (OA) is the most common arthritis and the leading cause of lower extremity disability in older adults. Understanding OA progression is important in the development of patient-specific therapeutic techniques at the early stage of OA rather than at the end stage. Histopathology scoring systems are usually used to evaluate OA progress and the mechanisms involved in the development of OA. This study aims to classify the histopathological images of cartilage specimens automatically, using artificial intelligence algorithms. Hematoxylin and eosin (HE)- and safranin O and fast green (SafO)-stained images of human cartilage specimens were divided into early, mild, moderate, and severe OA. Five pre-trained convolutional networks (DarkNet-19, MobileNet, ResNet-101, NasNet) were utilized to extract the twenty features from the last fully connected layers for both scenarios of SafO and HE. Principal component analysis (PCA) and ant lion optimization (ALO) were utilized to obtain the best-weighted features. The support vector machine classifier was trained and tested based on the selected descriptors to achieve the highest accuracies of 98.04% and 97.03% in HE and SafO, respectively. Using the ALO algorithm, the F1 scores were 0.97, 0.991, 1, and 1 for the HE images and 1, 0.991, 0.97, and 1 for the SafO images for the early, mild, moderate, and severe classes, respectively. This algorithm may be a useful tool for researchers to evaluate the histopathological images of OA without the need for experts in histopathology scoring systems or the need to train new experts. Incorporating automated deep features could help to improve the characterization and understanding of OA progression and development.
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Affiliation(s)
- Ateka Khader
- Department of Biomedical Systems and Informatics Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan
| | - Hiam Alquran
- Department of Biomedical Systems and Informatics Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan
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Bonaldi L, Pretto A, Pirri C, Uccheddu F, Fontanella CG, Stecco C. Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies. Bioengineering (Basel) 2023; 10:bioengineering10020137. [PMID: 36829631 PMCID: PMC9952222 DOI: 10.3390/bioengineering10020137] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 01/13/2023] [Accepted: 01/17/2023] [Indexed: 01/22/2023] Open
Abstract
By leveraging the recent development of artificial intelligence algorithms, several medical sectors have benefited from using automatic segmentation tools from bioimaging to segment anatomical structures. Segmentation of the musculoskeletal system is key for studying alterations in anatomical tissue and supporting medical interventions. The clinical use of such tools requires an understanding of the proper method for interpreting data and evaluating their performance. The current systematic review aims to present the common bottlenecks for musculoskeletal structures analysis (e.g., small sample size, data inhomogeneity) and the related strategies utilized by different authors. A search was performed using the PUBMED database with the following keywords: deep learning, musculoskeletal system, segmentation. A total of 140 articles published up until February 2022 were obtained and analyzed according to the PRISMA framework in terms of anatomical structures, bioimaging techniques, pre/post-processing operations, training/validation/testing subset creation, network architecture, loss functions, performance indicators and so on. Several common trends emerged from this survey; however, the different methods need to be compared and discussed based on each specific case study (anatomical region, medical imaging acquisition setting, study population, etc.). These findings can be used to guide clinicians (as end users) to better understand the potential benefits and limitations of these tools.
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Affiliation(s)
- Lorenza Bonaldi
- Department of Civil, Environmental and Architectural Engineering, University of Padova, Via F. Marzolo 9, 35131 Padova, Italy
| | - Andrea Pretto
- Department of Industrial Engineering, University of Padova, Via Venezia 1, 35121 Padova, Italy
| | - Carmelo Pirri
- Department of Neuroscience, University of Padova, Via A. Gabelli 65, 35121 Padova, Italy
| | - Francesca Uccheddu
- Department of Industrial Engineering, University of Padova, Via Venezia 1, 35121 Padova, Italy
- Centre for Mechanics of Biological Materials (CMBM), University of Padova, Via F. Marzolo 9, 35131 Padova, Italy
| | - Chiara Giulia Fontanella
- Department of Industrial Engineering, University of Padova, Via Venezia 1, 35121 Padova, Italy
- Centre for Mechanics of Biological Materials (CMBM), University of Padova, Via F. Marzolo 9, 35131 Padova, Italy
- Correspondence: ; Tel.: +39-049-8276754
| | - Carla Stecco
- Department of Neuroscience, University of Padova, Via A. Gabelli 65, 35121 Padova, Italy
- Centre for Mechanics of Biological Materials (CMBM), University of Padova, Via F. Marzolo 9, 35131 Padova, Italy
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Hirvasniemi J, Runhaar J, van der Heijden RA, Zokaeinikoo M, Yang M, Li X, Tan J, Rajamohan HR, Zhou Y, Deniz CM, Caliva F, Iriondo C, Lee JJ, Liu F, Martinez AM, Namiri N, Pedoia V, Panfilov E, Bayramoglu N, Nguyen HH, Nieminen MT, Saarakkala S, Tiulpin A, Lin E, Li A, Li V, Dam EB, Chaudhari AS, Kijowski R, Bierma-Zeinstra S, Oei EHG, Klein S. The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images. Osteoarthritis Cartilage 2023; 31:115-125. [PMID: 36243308 DOI: 10.1016/j.joca.2022.10.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 09/02/2022] [Accepted: 10/03/2022] [Indexed: 11/05/2022]
Abstract
OBJECTIVES The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. DESIGN The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). RESULTS Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57-0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52-0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. CONCLUSION The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.
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Affiliation(s)
- J Hirvasniemi
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
| | - J Runhaar
- Department of General Practice, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - R A van der Heijden
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - M Zokaeinikoo
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, USA
| | - M Yang
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, USA
| | - X Li
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, USA
| | - J Tan
- Department of Radiology, New York University Langone Health, New York, USA
| | - H R Rajamohan
- Department of Radiology, New York University Langone Health, New York, USA
| | - Y Zhou
- Department of Radiology, New York University Langone Health, New York, USA
| | - C M Deniz
- Department of Radiology, New York University Langone Health, New York, USA
| | - F Caliva
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - C Iriondo
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - J J Lee
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - F Liu
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - A M Martinez
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - N Namiri
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - V Pedoia
- Department of Radiology, University of California, San Francisco, San Francisco, USA
| | - E Panfilov
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - N Bayramoglu
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - H H Nguyen
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - M T Nieminen
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - S Saarakkala
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - A Tiulpin
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
| | - E Lin
- Akousist Co., Ltd., Taoyuan City, Taiwan
| | - A Li
- Akousist Co., Ltd., Taoyuan City, Taiwan
| | - V Li
- Akousist Co., Ltd., Taoyuan City, Taiwan
| | - E B Dam
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - A S Chaudhari
- Department of Radiology, Stanford University, Stanford, USA
| | - R Kijowski
- Department of Radiology, New York University Langone Health, New York, USA
| | - S Bierma-Zeinstra
- Department of General Practice, Erasmus MC University Medical Center, Rotterdam, the Netherlands; Department of Orthopedics & Sport Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - E H G Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - S Klein
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
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16
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Chadoulos CG, Tsaopoulos DE, Moustakidis S, Tsakiridis NL, Theocharis JB. A novel multi-atlas segmentation approach under the semi-supervised learning framework: Application to knee cartilage segmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 227:107208. [PMID: 36384059 DOI: 10.1016/j.cmpb.2022.107208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 10/19/2022] [Accepted: 10/27/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Multi-atlas based segmentation techniques, which rely on an atlas library comprised of training images labeled by an expert, have proven their effectiveness in multiple automatic segmentation applications. However, the usage of exhaustive patch libraries combined with the voxel-wise labeling incur a large computational cost in terms of memory requirements and execution times. METHODS To confront this shortcoming, we propose a novel two-stage multi-atlas approach designed under the Semi-Supervised Learning (SSL) framework. The main properties of our method are as follows: First, instead of the voxel-wise labeling approach, the labeling of target voxels is accomplished here by exploiting the spectral content of globally sampled datasets from the target image, along with their spatially correspondent data collected from the atlases. Following SSL, voxels classification is boosted by incorporating unlabeled data from the target image, in addition to the labeled ones from atlas library. Our scheme integrates constructively fruitful concepts, including sparse reconstructions of voxels from linear neighborhoods, HOG feature descriptors of patches/regions, and label propagation via sparse graph constructions. Segmentation of the target image is carried out in two stages: stage-1 focuses on the sampling and labeling of global data, while stage-2 undertakes the above tasks for the out-of-sample data. Finally, we propose different graph-based methods for the labeling of global data, while these methods are extended to deal with the out-of-sample voxels. RESULTS A thorough experimental investigation is conducted on 76 subjects provided by the publicly accessible Osteoarthritis Initiative (OAI) repository. Comparative results and statistical analysis demonstrate that the suggested methodology exhibits superior segmentation performance compared to the existing patch-based methods, across all evaluation metrics (DSC:88.89%, Precision: 89.86%, Recall: 88.12%), while at the same time it requires a considerably reduced computational load (>70% reduction on average execution time with respect to other patch-based). In addition, our approach is favorably compared against other non patch-based and deep learning methods in terms of performance accuracy (on the 3-class problem). A final experimentation on a 5-class setting of the problems demonstrates that our approach is capable of achieving performance comparable to existing state-of-the-art knee cartilage segmentation methods (DSC:88.22% and DSC:85.84% for femoral and tibial cartilage respectively).
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Affiliation(s)
- Christos G Chadoulos
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece.
| | - Dimitrios E Tsaopoulos
- Institute for Bio-Economy and Agri-Technology, Centre for Research and Technology Hellas, Volos, 38333, Greece.
| | | | - Nikolaos L Tsakiridis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece.
| | - John B Theocharis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece.
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Xie D, Murray J, Lartey R, Gaj S, Kim J, Li M, Eck BL, Winalski CS, Altahawi F, Jones MH, Obuchowski NA, Huston LJ, Harkins KD, Friel HT, Damon BM, Knopp MV, Kaeding CC, Spindler KP, Li X. Multi-vendor multi-site quantitative MRI analysis of cartilage degeneration 10 Years after anterior cruciate ligament reconstruction: MOON-MRI protocol and preliminary results. Osteoarthritis Cartilage 2022; 30:1647-1657. [PMID: 36049665 PMCID: PMC9671830 DOI: 10.1016/j.joca.2022.08.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 07/12/2022] [Accepted: 08/01/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To describe the protocol of a multi-vendor, multi-site quantitative MRI study for knee post-traumatic osteoarthritis (PTOA), and to present preliminary results of cartilage degeneration using MR T1ρ and T2 imaging 10 years after anterior cruciate ligament reconstruction (ACLR). DESIGN This study involves three sites and two MR platforms. The patients are from a nested cohort (termed as Onsite cohort) within the Multicenter Orthopaedic Outcomes Network (MOON) cohort 10 years after ACLR. Phantoms and controls were scanned for evaluating reproducibility. Cartilage was automatically segmented, and T1ρ and T2 were compared between operated, contralateral, and control knees. RESULTS Sixty-eight ACL-reconstructed patients and 20 healthy controls were included. In phantoms, the intra-site coefficients of variation (CVs) of repeated scans ranged 1.8-2.1% for T1ρ and 1.3-1.7% for T2. The inter-site CVs ranged 1.6-2.1% for T1ρ and 1.1-1.4% for T2. In human subjects, the intra-site scan/rescan CVs ranged 2.2-3.5% for T1ρ and 2.6-4.9% for T2 for the six major compartments. In patients, operated knees showed significantly higher T1ρ and T2 values mainly in medial femoral condyle, medial tibia and trochlear cartilage compared with contralateral knees, and showed significantly higer T1ρ and T2 values in all six compartments compared to healthy control knees. The patient contralateral knees showed higher T1ρ and T2 values mainly in the lateral femoral condyle, lateral tibia, trochlear, and patellar cartilage compared to healthy control knees. CONCLUSION A platform and workflow with rigorous quality control has been established for a multi-vendor multi-site quantitative MRI study in evaluating PTOA 10 years after ACLR. Our preliminary report suggests significant cartilage matrix changes in both operated and contralateral knees compared with healthy control knees.
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Affiliation(s)
- D Xie
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, USA; Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA; Department of Orthopaedics, Xiangya Hospital, Central South University, Changsha, Hunan, China.
| | - J Murray
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, USA; Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
| | - R Lartey
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, USA; Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
| | - S Gaj
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, USA; Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
| | - J Kim
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, USA; Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
| | - M Li
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, USA; Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
| | - B L Eck
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, USA; Department of Diagnostic Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, USA.
| | - C S Winalski
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, USA; Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA; Department of Diagnostic Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, USA.
| | - F Altahawi
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, USA; Department of Diagnostic Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, USA.
| | - M H Jones
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - N A Obuchowski
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, USA; Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
| | - L J Huston
- Department of Orthopaedics and Rehabilitation, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - K D Harkins
- Departments of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - H T Friel
- MR Clinical Science, Philips Healthcare, Highland Heights, OH, USA.
| | - B M Damon
- Departments of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - M V Knopp
- Wright Center of Innovation in Biomedical Imaging, Department of Radiology, The Ohio State University, Columbus, OH, USA.
| | - C C Kaeding
- Department of Orthopaedic Surgery, The Ohio State University, Columbus, OH, USA.
| | - K P Spindler
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, USA; Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, OH, USA.
| | - X Li
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, USA; Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA; Department of Diagnostic Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, USA.
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Tiulpin A, Saarakkala S, Mathiessen A, Hammer HB, Furnes O, Nordsletten L, Englund M, Magnusson K. Predicting total knee arthroplasty from ultrasonography using machine learning. OSTEOARTHRITIS AND CARTILAGE OPEN 2022; 4:100319. [DOI: 10.1016/j.ocarto.2022.100319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 10/15/2022] [Accepted: 10/30/2022] [Indexed: 11/09/2022] Open
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19
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Väärälä A, Casula V, Peuna A, Panfilov E, Mobasheri A, Haapea M, Lammentausta E, Nieminen MT. Predicting osteoarthritis onset and progression with 3D texture analysis of cartilage MRI DESS: 6-Year data from osteoarthritis initiative. J Orthop Res 2022; 40:2597-2608. [PMID: 35152476 PMCID: PMC9790756 DOI: 10.1002/jor.25293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 11/13/2021] [Accepted: 02/02/2022] [Indexed: 02/04/2023]
Abstract
In this study, we developed a gray level co-occurrence matrix-based 3D texture analysis method for dual-echo steady-state (DESS) magnetic resonance (MR) images to be used for knee cartilage analysis in osteoarthritis (OA) studies and use it to study changes in articular cartilage between different subpopulations based on their rate of progression into radiographically confirmed OA. In total, 642 series of right knee DESS MR images at 3T were obtained from baseline, 36- and 72-month follow-ups from the OA Initiative database. At baseline, all 214 subjects included in the study had Kellgren-Lawrence (KL) grade <2. Three groups were defined, based on time of progression into radiographic OA (ROA) (KL grades ≥2): control (no progression), fast progressor (ROA at 36 months), and slow progressor (ROA at 72 months) groups. 3D texture analysis was used to extract textural features for femoral and tibial cartilages. All textural features, in both femur and tibia, showed significant longitudinal changes across all groups and tissue layers. Most of the longitudinal changes were observed in progressors, but significant changes were observed also in controls. Differences between groups were mostly seen at baseline and 72 months. The method is sensitive to cartilage changes before and after ROA. It was able to detect longitudinal changes in controls and progressors and to distinguish cartilage alterations due to OA and aging. Moreover, it was able to distinguish controls and different progressor groups before any radiographic signs of OA and during OA. Thus, texture analysis could be used as a marker for the onset and progression of OA.
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Affiliation(s)
- Ari Väärälä
- Research Unit of Medical Imaging, Physics and TechnologyUniversity of OuluOuluFinland
| | - Victor Casula
- Research Unit of Medical Imaging, Physics and TechnologyUniversity of OuluOuluFinland,Medical Research CenterUniversity of Oulu and Oulu University HospitalOuluFinland
| | - Arttu Peuna
- Research Unit of Medical Imaging, Physics and TechnologyUniversity of OuluOuluFinland,Medical Research CenterUniversity of Oulu and Oulu University HospitalOuluFinland,Department of Medical ImagingCentral Finland Central HospitalJyväskyläFinland
| | - Egor Panfilov
- Research Unit of Medical Imaging, Physics and TechnologyUniversity of OuluOuluFinland
| | - Ali Mobasheri
- Research Unit of Medical Imaging, Physics and TechnologyUniversity of OuluOuluFinland,Department of Regenerative MedicineState Research Institute Centre for Innovative MedicineVilniusLithuania,Departments of Orthopedics, Rheumatology and Clinical ImmunologyUniversity Medical Center UtrechtUtrechtThe Netherlands,Department of Joint SurgeryThe First Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouGuangdongChina
| | - Marianne Haapea
- Medical Research CenterUniversity of Oulu and Oulu University HospitalOuluFinland,Department of Diagnostic RadiologyOulu University HospitalOuluFinland
| | - Eveliina Lammentausta
- Research Unit of Medical Imaging, Physics and TechnologyUniversity of OuluOuluFinland,Department of Diagnostic RadiologyOulu University HospitalOuluFinland
| | - Miika T. Nieminen
- Research Unit of Medical Imaging, Physics and TechnologyUniversity of OuluOuluFinland,Medical Research CenterUniversity of Oulu and Oulu University HospitalOuluFinland,Department of Diagnostic RadiologyOulu University HospitalOuluFinland
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Xiongfeng T, Yingzhi L, Xianyue S, Meng H, Bo C, Deming G, Yanguo Q. Automated detection of knee cystic lesions on magnetic resonance imaging using deep learning. Front Med (Lausanne) 2022; 9:928642. [PMID: 36016997 PMCID: PMC9397605 DOI: 10.3389/fmed.2022.928642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 07/11/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundCystic lesions are frequently observed in knee joint diseases and are usually associated with joint pain, degenerative disorders, or acute injury. Magnetic resonance imaging-based, artificial intelligence-assisted cyst detection is an effective method to improve the whole knee joint analysis. However, few studies have investigated this method. This study is the first attempt at auto-detection of knee cysts based on deep learning methods.MethodsThis retrospective study collected data from 282 subjects with knee cysts confirmed at our institution from January to October 2021. A Squeeze-and-Excitation (SE) inception attention-based You only look once version 5 (SE-YOLOv5) model was developed based on a self-attention mechanism for knee cyst-like lesion detection and differentiation from knee effusions, both characterized by high T2-weighted signals in magnetic resonance imaging (MRI) scans. Model performance was evaluated via metrics including accuracy, precision, recall, mean average precision (mAP), F1 score, and frames per second (fps).ResultsThe deep learning model could accurately identify knee MRI scans and auto-detect both obvious cyst lesions and small ones with inconspicuous contrasts. The SE-YOLO V5 model constructed in this study yielded superior performance (F1 = 0.879, precision = 0.887, recall = 0.872, all class mAP0.5 = 0.944, effusion mAP = 0.945, cyst mAP = 0.942) and improved detection speed compared to a traditional YOLO model.ConclusionThis proof-of-concept study examined whether deep learning models could detect knee cysts and distinguish them from knee effusions. The results demonstrated that the classical Yolo V5 and proposed SE-Yolo V5 models could accurately identify cysts.
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