Modified radial-search algorithm for segmentation of tibiofemoral cartilage in MR images of patients with subchondral lesion

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

doi:10.1007/s11548-020-02116-z

Cited by 3 publications

(8 citation statements)

References 24 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%

“…The projection of whole segmented cartilage on a 2D plane is called 2D-WearMap. 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.…”

Section: D-wearmap Generationmentioning

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%

“…(ii) Select the seed point and implement inner femur cartilage boundary detection using the recently reported modified radial search study. 27 (iii) From the inner boundary points, select corresponding outer boundary points with a long-fixed length of 8 mm, including anterior-posterior meniscus horns of the image. This 8 mm value was selected based upon inputs from radiologists so that meniscus horns can be included.…”

Section: Selection Of Subregion Roismentioning

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: D-wearmap Generationmentioning

confidence: 99%

Section: Selection Of Subregion Roismentioning

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

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From classical to deep learning: review on cartilage and bone segmentation techniques in knee osteoarthritis research

et al. 2020

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Validation of a Method to Measure the T2 Value from the Color Mapping by Hue Value

Hananouchi

Nakayama

2023

CMIR

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Background: Color mapping using quantitative MRI (Magnetic Resonance Imaging) is now being reported in various medical fields to be useful in showing tissue conditions and morphological perspectives. Specifically, T2 mapping as one of the color mapping has been used to describe cartilage conditions in orthopedics. However, for orthopedic physicians in out-patient clinics, the color mapping shows only the colors on the mapping to patients without explaining their numerical values. Methods: To address this issue, our study proposed an approach to measure T2 values based on the hue value converted from Red, Green, Blue information on the processed color map. We evaluated the validity of our method with 25 subjects. Results: Our proposed method showed a good and high correlation coefficient (r = 0.9924, p < 0.0001), and the difference of the T2 values between using a dedicated software on console of MRI scanner and our method was small (its absolute value was approximately 1.5, p = 0.008). Conclusion: Therefore, we consider the proposed method is an alternative approach to show the T2 value when the color mapping are available.

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Modified radial-search algorithm for segmentation of tibiofemoral cartilage in MR images of patients with subchondral lesion

Cited by 3 publications

References 24 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

From classical to deep learning: review on cartilage and bone segmentation techniques in knee osteoarthritis research

Validation of a Method to Measure the T2 Value from the Color Mapping by Hue Value

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