Segmentation of Knee Cartilage: A Comprehensive Review

Kubíček, Jan; Penhaker, Marek; Augustýnek, Martin; Bryjova, Iveta; Černý, Martin

doi:10.1166/jmihi.2018.2376

Cited by 14 publications

(11 citation statements)

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“…These methods commonly require the initialization, meaning that the user is required to specify certain parameters driving the segmentation process. The semi-automatic methods may be divided as follows: Intensity-based [27], thresholding, watershed [28], edge detection [29], energy minimization [30], Live Wire [31], and active contours [30,32].…”

Section: Recent Methods Of Cartilage Segmentationmentioning

confidence: 99%

Segmentation of Articular Cartilage and Early Osteoarthritis based on the Fuzzy Soft Thresholding Approach Driven by Modified Evolutionary ABC Optimization and Local Statistical Aggregation

et al. 2019

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Articular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel’s classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel’s membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise.

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Section: Recent Methods Of Cartilage Segmentationmentioning

confidence: 99%

Segmentation of Articular Cartilage and Early Osteoarthritis based on the Fuzzy Soft Thresholding Approach Driven by Modified Evolutionary ABC Optimization and Local Statistical Aggregation

et al. 2019

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“…A number of review articles on knee joint segmentation have been published in recent years [35,36,37,38]. Aprovitola and Gallo [35] and Zhang et al [38] looked into knee bone segmentation, whereas only Zhang et al [38] covered segmentation strategies of articular cartilage, but without a quantitative comparison of the methods.…”

Section: Related Workmentioning

confidence: 99%

“…Aprovitola and Gallo [35] and Zhang et al [38] looked into knee bone segmentation, whereas only Zhang et al [38] covered segmentation strategies of articular cartilage, but without a quantitative comparison of the methods. Other work in knee articular cartilage segmentation probed into segmentation strategies based on the level of automation and only covered a subset of methods [23,37]. Kumar et al [23] quantitatively reviewed semi-automatic and automatic methods in summary tables with limited detail and scope but lacked an analysis of results.…”

Section: Related Workmentioning

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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“…Kubicek et al. [19] and Kumar et al. [20] worked on reviewing the knee cartilage segmentation by classifying the methods into manual, semi‐automatic, and automatic.…”

Section: Related Work and Our Contributionmentioning

confidence: 99%

Review of automated segmentation approaches for knee images

Ridhma

Kaur

Sofat

et al. 2020

IET Image Processing

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Knee disorders are common among the human population. Knee osteoarthritis (OA) is the most widespread knee joint disorder, which may require surgical treatment. The detection and diagnosis of knee joint disorders from medical images demand enormous human effort and time. The development of a computer‐aided diagnosis (CAD) system can notably minimise the burden of medical experts and remove the intra‐observer and inter‐observer variations. To achieve the goal, the highly challenging research problem of knee image segmentation has been frequently paid attention in past years, which can be efficiently applied in the development of the CAD system. Knee image segmentation is a challenging task owing to the image contrasts, intensity variations, shape irregularities, and the presence of thin cartilage structures. Therefore, this paper presents a literature review of automated segmentation approaches mainly focused on the segmentation of knee cartilage and bone, with respect to the underlying technical aspects, datasets used, and the performance reported. The paper also presents the growth from classical segmentation approaches towards the deep learning approaches in the knee image segmentation. Owing to the varying quality and complexity of different knee image datasets, this paper abstains from doing a rigorous comparative evaluation of image segmentation approaches.

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Segmentation of Knee Cartilage: A Comprehensive Review

Cited by 14 publications

References 0 publications

Segmentation of Articular Cartilage and Early Osteoarthritis based on the Fuzzy Soft Thresholding Approach Driven by Modified Evolutionary ABC Optimization and Local Statistical Aggregation

Segmentation of Articular Cartilage and Early Osteoarthritis based on the Fuzzy Soft Thresholding Approach Driven by Modified Evolutionary ABC Optimization and Local Statistical Aggregation

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

Review of automated segmentation approaches for knee images

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