Classification of MRI images in 2D coronal view and measurement of articular cartilage thickness for early detection of knee osteoarthritis

Kumar, Vijaya B.; Jayanthy, A. K.

doi:10.1109/rteict.2016.7808167

Cited by 11 publications

(6 citation statements)

References 8 publications

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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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“…As a non-invasive modality with high spatial resolution without ionizing radiation and multi-planar capability, MRI can capture useful structures of the joint, e.g., cartilage morphology and biochemical composition [40], [41]. On the other hand, MRI is very sensitive to full or by the success of HGE [10], [14], [47]- [49] [46]. Even though these approaches cut off details by smoothing, they are merely applicable for classifying the thickness of cartilages.…”

Section: B Magnetic Resonance Imagingmentioning

confidence: 99%

“…The maturity of radiographic segmentation and edge detection approaches still act as a leading position for MRI segmentation. Followed by masking, Canny edge detection is applied for: i) edge detection by identifying local maxima of the image gradient [10], [47], ii) generating segmented cartilage (i.e., ROI). MRI segmentation based on classification is widely used, where each voxel is classified into two classes, e.g., sliced 3D MRIs into three sets of 2D images (i.e., across X, Y, Z axes).…”

Section: B Magnetic Resonance Imagingmentioning

confidence: 99%

“…Available biomedical imaging modalities include ultrasound, mammograms, Xray, computed tomography (CT), magnetic resonance imaging (MRI), electroencephalography, electrocardiography, endoscopy, elastography, magnetoencephalography, tactile imaging, thermograph, and nuclear medicine functional imaging [8]. CT and radiographs exhibit bone shapes well but carry less information on soft-tissue regions, which can be important for OA diagnosis [9], [10]. MRIs, in turn, are particularly useful for the identification of full or partial-thickness changes of articular cartilage, but can not capture bone architectures well, which are a potential indicator for an earliest OA progression [2], [9], [11].…”

Section: Introductionmentioning

confidence: 99%

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DeepKneeExplainer: Explainable Knee Osteoarthritis Diagnosis From Radiographs and Magnetic Resonance Imaging

et al. 2021

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Osteoarthritis (OA) is a degenerative joint disease, which significantly affects middleaged and elderly people. Although primarily identified via hyaline cartilage change based on medical images, technical bottlenecks like noise, artifacts, and modality impose an enormous challenge on high-precision, objective, and efficient early quantification of OA. Owing to recent advancements, approaches based on neural networks (DNNs) have shown outstanding success in this application domain. However, due to nested non-linear and complex structures, DNNs are mostly opaque and perceived as black-box methods, which raises numerous legal and ethical concerns. Moreover, these approaches do not have the ability to provide the reasoning behind diagnosis decisions in the way humans would do, which poses an additional risk in the clinical setting. In this paper, we propose a novel explainable method for knee OA diagnosis based on radiographs and magnetic resonance imaging (MRI), which we called DeepKneeExplainer. First, we comprehensively preprocess MRIs and radiographs through the deep-stacked transformation technique against possible noises and artifacts that could contain unseen images for domain generalization. Then, we extract the region of interests (ROIs) by employing U-Net architecture with ResNet backbone. To classify the cohorts, we train DenseNet and VGG architectures on the extracted ROIs. Finally, we highlight classdiscriminating regions using gradient-guided class activation maps (Grad-CAM++) and layerwise relevance propagation (LRP), followed by providing human-interpretable explanations of the predictions. Comprehensive experiments based on the multicenter osteoarthritis study (MOST) cohorts, our approach yields up to 91% classification accuracy, outperforming comparable stateof-the-art approaches. We hope that our results will encourage medical researchers and developers to adopt explainable methods and DNN-based analytic pipelines towards an increasing acceptance and adoption of AI-assisted applications in the clinical practice for improved knee OA diagnoses.

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“…The human skeletal system is a persistent framework that plays a key role in retaining the shape of the body and enabling its movement. Joints are the points where bones are connected for Osteoarthritis Disease Diagnosis to a layer of soft tissue known as ligaments [1]. Ligaments are fibrous connective tissues that form a thin layer over the junction of joints to prevent friction between the bones and prevent abnormal movement [2].…”

Section: Introductionmentioning

confidence: 99%

Prediction Model Using Reinforcement Deep Learning Technique for Osteoarthritis Disease Diagnosis

Kanthavel¹,

Dhaya²

2022

Computer Systems Science and Engineering

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Osteoarthritis is the most common class of arthritis that involves tears down the soft cartilage between the joints of the knee. The regeneration of this cartilage tissue is not possible, and thus physicians typically suggest therapeutic measures to prevent further deterioration over time. Normally, bringing about joint replacement is a remedial course of action. Expose itself in joint pain recognized with a normal X-ray. Deep learning plays a vital role in predicting the early stages of osteoarthritis by using the MRI pictures of muscles of the knee muscle. It can be used to accurately measure the shape and texture of biological structures can be measured consistently from X-ray images. Moreover, deep learning-based computation can be used to design framework to predict whether a given patient will develop osteoarthritis. Such a framework can identify clear biochemical changes in the focal point of ligaments of the knees of patients who have exhibit pre-indications in standard imaging. This study proposes framework to identify cases of osteoarthritis by using deep learning and reinforcement learning. It can be used as a clinical mechanism to predict the occurrence of osteoarthritis so that patients can benefit from early intervention.

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Classification of MRI images in 2D coronal view and measurement of articular cartilage thickness for early detection of knee osteoarthritis

Cited by 11 publications

References 8 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

DeepKneeExplainer: Explainable Knee Osteoarthritis Diagnosis From Radiographs and Magnetic Resonance Imaging

Prediction Model Using Reinforcement Deep Learning Technique for Osteoarthritis Disease Diagnosis

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