3-D segmentation of articular cartilages by graph cuts using knee MR images from osteoarthritis initiative

Shim, Hackjoon; Lee, Soochahn; Kim, Bohyeong; Tao, Cheng; Chang, Sun Ju; Yun, Il Dong; Lee, Sang Uk; Kwoh, C. Kent; Bae, Kyongtae T.

doi:10.1117/12.770887

Cited by 6 publications

(5 citation statements)

References 29 publications

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“…One type of seeds is used to specify cartilage, while the other is placed over adjacent non-cartilage background tissues (bone, joint fluid, menisci, etc.). Some authors have used s/t Gcut methods for cartilage segmentation [48,147,148]. Figure 12 shows segmentation results using this method.…”

Section: Graph-based Methodsmentioning

confidence: 99%

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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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Section: Graph-based Methodsmentioning

confidence: 99%

“…One popular approach for segmentation of articular cartilage is graph-cuts (Gcuts) that are commonly applied for automatic or semi-automatic segmentation. In 2008, Gcuts were used for semi-automatic segmentation of articular cartilage [48]. Atlas-based segmentation is another popular method widely used in this domain [49].…”

Section: Overviewmentioning

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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“…Semi-quantitative methods were also developed and used different modalities, including active contour and shape models, 74 – 76 atlas-based models, 77 B-spline snakes, 78 graph cuts, 79 k-nearest neighbor, 80 and 3D Euclidean distance transformation. 81 After MRI acquisition, the segmentation is performed using pre-processing (noise removal, normalization, etc.…”

Section: Mri Assessments Enabling Visualization and Quantification Of...mentioning

confidence: 99%

Magnetic resonance imaging assessments for knee segmentation and their use in combination with machine/deep learning as predictors of early osteoarthritis diagnosis and prognosis

Martel‐Pelletier

Paiement

Pelletier

2023

Therapeutic Advances in Musculoskeletal

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Knee osteoarthritis (OA) is a prevalent and disabling disease that can develop over decades. This disease is heterogeneous and involves structural changes in the whole joint, encompassing multiple tissue types. Detecting OA before the onset of irreversible changes is crucial for early management, and this could be achieved by allowing knee tissue visualization and quantifying their changes over time. Although some imaging modalities are available for knee structure assessment, magnetic resonance imaging (MRI) is preferred. This narrative review looks at existing literature, first on MRI-developed approaches for evaluating knee articular tissues, and second on prediction using machine/deep-learning-based methodologies and MRI as input or outcome for early OA diagnosis and prognosis. A substantial number of MRI methodologies have been developed to assess several knee tissues in a semi-quantitative and quantitative fashion using manual, semi-automated and fully automated systems. This dynamic field has grown substantially since the advent of machine/deep learning. Another active area is predictive modelling using machine/deep-learning methodologies enabling robust early OA diagnosis/prognosis. Moreover, incorporating MRI markers as input/outcome in such predictive models is important for a more accurate OA structural diagnosis/prognosis. The main limitation of their usage is the ability to move them in rheumatology practice. In conclusion, MRI knee tissue determination and quantification provide early indicators for individuals at high risk of developing this disease or for patient prognosis. Such assessment of knee tissues, combined with the development of models/tools from machine/deep learning using, in addition to other parameters, MRI markers for early diagnosis/prognosis, will maximize opportunities for individualized risk assessment for use in clinical practice permitting precision medicine. Future efforts should be made to integrate such prediction models into open access, allowing early disease management to prevent or delay the OA outcome.

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“…In fully automated technique, the cartilage segmentation has been shown to be exceedingly difficult due to cartilage signal heterogeneity, low tissuecontrast and shape irregularity particularly in advanced OA cartilage [8]. Thus, we use semi-automatic segmentation technique based on graph cuts for cartilage extraction [9]. The corresponding bone structure was extracted by a regionbased semi-automatic segmentation.…”

Section: Bone and Cartilage Extractionmentioning

confidence: 99%

Automatic bone registration in MR knee images for cartilage morphological analysis

et al. 2009

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We propose a cartilage matching technique based on the registration of the corresponding bone structures instead of using the cartilage. Our method consists of five steps. First, cartilage and corresponding bone structures are extracted by semi-automatic segmentation. Second, gross translational mismatch between corresponding bone structures is corrected by point-based rough registration. The center of inertia (COI) of each segmented bone structure is considered as the reference point. Third, the initial alignment is refined by distance-based surface registration. For fast and robust convergence of the distance measure to the optimal value, a 3D distance map is generated by the Gaussian-weighted narrow-band distance propagation. Fourth, rigid transformation of the bone surface registration is applied to the cartilage of baseline MR images. Finally, morphological differences of the corresponding cartilages are visualized by color-coded mapping and image fusion. Experimental results show that the cartilage morphological changes of baseline and followup MR knee images can be easily recognized by the correct registration of the corresponding bones.

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3-D segmentation of articular cartilages by graph cuts using knee MR images from osteoarthritis initiative

Cited by 6 publications

References 29 publications

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

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

Magnetic resonance imaging assessments for knee segmentation and their use in combination with machine/deep learning as predictors of early osteoarthritis diagnosis and prognosis

Automatic bone registration in MR knee images for cartilage morphological analysis

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