Automated Segmentation of Knee Cartilage Using Modified Radial Approach for OA Patients with and without Bone Abnormality

Thaha, Rafeek; Jogi, Sandeep Panwar; Rajan, Sriram; Mahajan, Vidur; Mehndiratta, Amit; Singh, Anup

doi:10.1109/iecbes.2018.8626718

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…In patients with OA, femur cartilage was semi-automatically segmented using the sagittal orientation of T2-weighted images using an in-house developed modified radial search algorithm. 27,28 In the current study, FS-PD-FSE images were used for the subangle detection process and the multi-echo T2-weighted images were used for the segmentation and generation of 2D-WearMap of femur cartilage.…”

Section: Cartilage Segmentationmentioning

confidence: 99%

“…26,27 In the OA patient group, 2D-WearMaps of T2 and thickness values of each subject were generated from the segmented cartilage from sagittal view images semi-automatically based on the radial-line method using our recently reported work. 27,28 In the AS-volunteers group, the 2D-WearMaps were generated from the manually segmented cartilage in coronal view images using the same algorithm. For obtaining the isotropic resolution (0.54 × 0.54 × 0.54 mm 3 ) of 2D-WearMaps, the number of slices in the map were interpolated using the inbuilt function "imresize" available in MATLAB.…”

Section: D-wearmap Generationmentioning

confidence: 99%

“…Also, a recently developed two-dimensional (2D)-WearMap technique (projection of whole cartilage present in patient data on a 2D plane) might be useful for the easy quantitative evaluation of cartilage. [26][27][28] The objectives of the current study were to develop a semiautomatic framework for quantitative analysis of biochemical properties and thickness of femur cartilage using MR-images and evaluate its potential for femur cartilage classification into healthy versus OA as well as patients with early OA versus advanced OA.…”

Section: Introductionmentioning

confidence: 99%

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A semi‐automatic framework based upon quantitative analysis of MR‐images for classification of femur cartilage into asymptomatic, early OA, and advanced‐OA groups

Thaha

Jogi

Rajan³

et al. 2021

Journal Orthopaedic Research

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To develop a semi-automatic framework for quantitative analysis of biochemical properties and thickness of femur cartilage using magnetic resonance (MR) images and evaluate its potential for femur cartilage classification into asymptomatic (AS), early osteoarthritis (OA), and advanced OA groups. In this study, knee joint MRI data (fat suppressed-proton density-weighted and multi-echo T2-weighted images) of eight AS-volunteers (data acquired twice) and 34 OA patients including 20 early OA (16 Grade-I and 4 Grade-II), 14 advanced-OA (Grade-III) were acquired at 3.0T MR scanner. Modified Outerbridge classification criteria was performed for the clinical evaluation of data by an experienced radiologist. Cartilage segmentation, T2mapping, 2D-WearMap generation, and subregion analysis were performed semiautomatically using in-house developed algorithms. The intraclass correlation coefficient (ICC) and coefficient of variation (CV) were computed for testing the reproducibility of T2 values. One-way analysis of variance with Tukey-Kramer post hoc test was performed for evaluating the differences among the groups. The performance of individual T2 and thickness, as well as their combination using logistic regression, were evaluated with receiver operating characteristics (ROC) curve analysis. The interscan agreement based on the ICC index was 0.95 and the CV was 2.45 ± 1.33%. T2 mean of values greater than 75th percentile showed sensitivity and specificity of 94.1% and 81.3% (AUC = 0.93, cut-off value = 47.9 ms) in differentiating AS volunteers versus OA group, while sensitivity and specificity of 90.0% and 81.3% (AUC = 0.90, cut-off value = 47.9 ms) in differentiating AS volunteers versus early OA groups, respectively. In the differentiation of early OA versus advanced-OA group, ROC results of combination (T2 and thickness) showed the highest sensitivity and specificity of 85.7%, and 70.0% (AUC = 0.79, cut-off value = 0.39) compared with individual T2 and thickness features, respectively. A computeraided quantitative evaluation of femur cartilage degeneration showed promising results and can be used to assist clinicians in diagnosing OA.

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Section: Cartilage Segmentationmentioning

confidence: 99%

Section: D-wearmap Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A semi‐automatic framework based upon quantitative analysis of MR‐images for classification of femur cartilage into asymptomatic, early OA, and advanced‐OA groups

Thaha

Jogi

Rajan³

et al. 2021

Journal Orthopaedic Research

Self Cite

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A Comprehensive Review on MRI-based Knee Joint Segmentation and Analysis Techniques

Mahendrakar

Kumar

Patil³

2023

CMIR

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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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Automated Segmentation of Knee Cartilage Using Modified Radial Approach for OA Patients with and without Bone Abnormality

Cited by 2 publications

References 6 publications

A semi‐automatic framework based upon quantitative analysis of MR‐images for classification of femur cartilage into asymptomatic, early OA, and advanced‐OA groups

A semi‐automatic framework based upon quantitative analysis of MR‐images for classification of femur cartilage into asymptomatic, early OA, and advanced‐OA groups

A Comprehensive Review on MRI-based Knee Joint Segmentation and Analysis Techniques

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