Knee Articular Cartilage Segmentation from MR Images

Kumar, Dileep; Gandhamal, Akash; Talbar, Sanjay N.; Hani, Ahmad Fadzil M.

doi:10.1145/3230631

Cited by 20 publications

(22 citation statements)

References 80 publications

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“…In quantitative knee MRI studies, manual, semiautomatic, or automatic segmentation approaches can be used for extraction of bone and cartilage surfaces. Automatic and semiautomatic segmentation algorithms provide more consistent labels of the knee structures in a shorter time than manual segmentation . For segmenting the knee bones and cartilages, many advanced techniques have been proposed, including but not limited to, deformable models, graph‐based methods, and voxel classification approaches .…”

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confidence: 99%

“…25 For segmenting the knee bones and cartilages, many advanced techniques have been proposed, including but not limited to, deformable models, graph-based methods, and voxel classification approaches. 25,26 In fact, successful approaches often combine several of these techniques into rather complex systems. More recently, deep-learning techniques 27,28 have reduced the complexity by rendering many preprocessing steps unnecessary, but for accurate and robust segmentation they require large training databases.…”

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confidence: 99%

“…Automatic and semiautomatic segmentation algorithms provide more consistent labels of the knee structures in a shorter time than manual segmentation . For segmenting the knee bones and cartilages, many advanced techniques have been proposed, including but not limited to, deformable models, graph‐based methods, and voxel classification approaches . In fact, successful approaches often combine several of these techniques into rather complex systems.…”

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confidence: 99%

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Quantification of patellofemoral cartilage deformation and contact area changes in response to static loading via high‐resolution MRI with prospective motion correction

Lange

Taghizadeh

Knowles

et al. 2019

Magnetic Resonance Imaging

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Background Higher‐resolution MRI of the patellofemoral cartilage under loading is hampered by subject motion since knee flexion is required during the scan. Purpose To demonstrate robust quantification of cartilage compression and contact area changes in response to in situ loading by means of MRI with prospective motion correction and regularized image postprocessing. Study Type Cohort study. Subjects Fifteen healthy male subjects. Field Strength 3 T. Sequence Spoiled 3D gradient‐echo sequence augmented with prospective motion correction based on optical tracking. Measurements were performed with three different loads (0/200/400 N). Assessment Bone and cartilage segmentation was performed manually and regularized with a deep‐learning approach. Average patellar and femoral cartilage thickness and contact area were calculated for the three loading situations. Reproducibility was assessed via repeated measurements in one subject. Statistical Tests Comparison of the three loading situations was performed by Wilcoxon signed‐rank tests. Results Regularization using a deep convolutional neural network reduced the variance of the quantified relative load‐induced changes of cartilage thickness and contact area compared to purely manual segmentation (average reduction of standard deviation by ∼50%) and repeated measurements performed on the same subject demonstrated high reproducibility of the method. For the three loading situations (0/200/400 N), the patellofemoral cartilage contact area as well as the mean patellar and femoral cartilage thickness were significantly different from each other (P < 0.05). While the patellofemoral cartilage contact area increased under loading (by 14.5/19.0% for loads of 200/400 N), patellar and femoral cartilage thickness exhibited a load‐dependent thickness decrease (patella: –4.4/–7.4%, femur: –3.4/–7.1% for loads of 200/400 N). Data Conclusion MRI with prospective motion correction enables quantitative evaluation of patellofemoral cartilage deformation and contact area changes in response to in situ loading. Regularizing the manual segmentations using a neural network enables robust quantification of the load‐induced changes. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1561–1570.

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Quantification of patellofemoral cartilage deformation and contact area changes in response to static loading via high‐resolution MRI with prospective motion correction

Lange

Taghizadeh

Knowles

et al. 2019

Magnetic Resonance Imaging

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“…The strength of the magnetic field in knee MRIs is another influencing factor that can contribute to improved clinical performance because of the higher signal-to-noise ratio (SNR) of an MR scanner. Theoretically, SNR is approximately linearly related to the magnetic field strength [23]. Higher SNR and increased contrast of the cartilage tissue to surrounding tissues, such as the bone, meniscus, and synovial fluid, is observed at 3-Tesla (3.0 T) MR scanner than that of an MR scanner with the most commonly used 1.5 T magnetic field strength in clinical standard [23,24].…”

Section: Imaging Modality and Articular Cartilage Quantificationmentioning

confidence: 99%

A review on segmentation of knee articular cartilage: from conventional methods towards deep learning

Ebrahimkhani

Jaward

Cicuttini

et al. 2020

Artificial Intelligence in Medicine

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In this paper, we review the state-of-the-art approaches for knee articular cartilage segmentation from conventional techniques to deep learning (DL) based techniques. Knee articular cartilage segmentation on magnetic resonance (MR) images is of great importance in early diagnosis of osteoarthritis (OA). Besides, segmentation allows estimating the articular cartilage loss rate which is utilised in clinical practice for assessing the disease progression and morphological changes. It has been traditionally applied in quantifying longitudinal knee OA progression pattern to detect and assess the articular cartilage thickness and volume. Topics covered include various image processing algorithms and major features of different segmentation techniques, feature computations and the performance evaluation metrics. This paper is intended to provide researchers with a broad overview of the currently existing methods in the field, as well as to highlight the shortcomings and potential considerations in the application at clinical practice. The survey showed that state-of-the-art techniques based on DL outperform the other segmentation methods. The analysis of the existing methods reveals that integration of DL-based algorithms with other traditional model-based approaches has achieved the best results (mean Dice similarity coefficient (DSC) between 85.8% and 90%).

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“…Due to the wide adoption of MR imaging methods, semiautomatic and automatic knee cartilage segmentation from MRI has been studied already for several decades, with more focus recently on purely automatic methods [33,24]. However, despite the availability of large imaging cohorts, such as Osteoarthritis Initiative (OAI) [30], large-scale analysis of such data in scope of OA research remains extremely challenging due to the lack of annotations.…”

Section: Related Workmentioning

confidence: 99%

Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation

Panfilov

Tiulpin

Klein

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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Degeneration of articular cartilage (AC) is actively studied in knee osteoarthritis (OA) research via magnetic resonance imaging (MRI). Segmentation of AC tissues from MRI data is an essential step in quantification of their damage. Deep learning (DL) based methods have shown potential in this realm and are the current state-of-the-art, however, their robustness to heterogeneity of MRI acquisition settings remains an open problem. In this study, we investigated two modern regularization techniques -mixup and adversarial unsupervised domain adaptation (UDA) -to improve the robustness of DL-based knee cartilage segmentation to new MRI acquisition settings. Our validation setup included two datasets produced by different MRI scanners and using distinct data acquisition protocols. We assessed the robustness of automatic segmentation by comparing mixup and UDA approaches to a strong baseline method at different OA severity stages and, additionally, in relation to anatomical locations. Our results showed that for moderate changes in knee MRI data acquisition settings both approaches may provide notable improvements in the robustness, which are consistent for all stages of the disease and affect the clinically important areas of the knee joint. However, mixup may be considered as a recommended approach, since it is more computationally efficient and does not require additional data from the target acquisition setup.

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Knee Articular Cartilage Segmentation from MR Images

Cited by 20 publications

References 80 publications

Quantification of patellofemoral cartilage deformation and contact area changes in response to static loading via high‐resolution MRI with prospective motion correction

Quantification of patellofemoral cartilage deformation and contact area changes in response to static loading via high‐resolution MRI with prospective motion correction

A review on segmentation of knee articular cartilage: from conventional methods towards deep learning

Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation

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