A semi-supervised multiview-MRI network for the detection of Knee Osteoarthritis.

Knee OsteoArthritis (OA) is a prevalent chronic condition, affecting a significant proportion of the global population. Detecting knee OA is crucial as the degeneration of the knee joint is irreversible. In this paper, we introduce a semi-supervised multi-view framework and a 3D CNN model for detecting knee OA using 3D Magnetic Resonance Imaging (MRI) scans.

A highly generalized classifier for osteoporosis radiography based on multiscale fractal, lacunarity, and entropy distributions.

Osteoporosis is a common degenerative disease with high incidence among aging populations. However, in regular radiographic diagnostics, asymptomatic osteoporosis is often overlooked and does not include tests for bone mineral density or bone trabecular condition. Therefore, we proposed a highly generalized classifier for osteoporosis radiography based on the multiscale fractal, lacunarity, and entropy distributions.

A discriminative shape-texture convolutional neural network for early diagnosis of knee osteoarthritis from X-ray images.

Knee Osteoarthritis (OA) is one of the most common causes of physical disability worldwide associated with a significant personal and socioeconomic burden. Deep Learning approaches based on Convolutional Neural Networks (CNNs) achieved remarkable improvements in knee OA detection. Despite this success, the problem of early knee OA diagnosis from plain radiographs remains a challenging task.

Prediction of knee osteoarthritis progression using radiological descriptors obtained from bone texture analysis and Siamese neural networks: data from OAI and MOST cohorts.

Background: Trabecular bone texture (TBT) analysis has been identified as an imaging biomarker that provides information on trabecular bone changes due to knee osteoarthritis (KOA). In parallel with the improvement in medical imaging technologies, machine learning methods have received growing interest in the scientific osteoarthritis community to potentially provide clinicians with prognostic data from conventional knee X-ray datasets, in particular from the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST) cohorts.

Patients And Methods: This study included 1888 patients from OAI and 683 patients from MOST cohorts.

Trabecular bone texture analysis of conventional radiographs in the assessment of knee osteoarthritis: review and viewpoint.

Background: Trabecular bone texture analysis (TBTA) has been identified as an imaging biomarker that provides information on trabecular bone changes due to knee osteoarthritis (KOA). Consequently, it is important to conduct a comprehensive review that would permit a better understanding of this unfamiliar image analysis technique in the area of KOA research. We examined how TBTA, conducted on knee radiographs, is associated to (i) KOA incidence and progression, (ii) total knee arthroplasty, and (iii) KOA treatment responses.

Integrative blockwise sparse analysis for tissue characterization and classification.

The topic of sparse representation of samples in high dimensional spaces has attracted growing interest during the past decade. In this work, we develop sparse representation-based methods for classification of clinical imaging patterns into healthy and diseased states. We propose a spatial block decomposition method to address irregularities of the approximation problem and to build an ensemble of classifiers that we expect to yield more accurate numerical solutions than conventional sparse analyses of the complete spatial domain of the images.

Discriminative Regularized Auto-Encoder for Early Detection of Knee OsteoArthritis: Data from the Osteoarthritis Initiative.

OsteoArthritis (OA) is the most common disorder of the musculoskeletal system and the major cause of reduced mobility among seniors. The visual evaluation of OA still suffers from subjectivity. Recently, Computer-Aided Diagnosis (CAD) systems based on learning methods showed potential for improving knee OA diagnostic accuracy.

Multifractal-based lacunarity analysis of trabecular bone in radiography.

This study presents textural characterization techniques for effective osteoporosis diagnosis using bone radiograph images. The automatic classification of osteoporosis and healthy (control) cases using bone radiograph images in this work presents a major challenge as the images show no visual differences for both cases. The proposed work utilizes multifractals to characterize the trabecular bone texture in the radiographs.

A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: Data from the OsteoArthritis Initiative.

This paper presents a fully developed computer aided diagnosis (CAD) system for early knee OsteoArthritis (OA) detection using knee X-ray imaging and machine learning algorithms. The X-ray images are first preprocessed in the Fourier domain using a circular Fourier filter. Then, a novel normalization method based on predictive modeling using multivariate linear regression (MLR) is applied to the data in order to reduce the variability between OA and healthy subjects.

Ischemic stroke enhancement using a variational model and the expectation maximization method.

Objectives: In order to enable less experienced physicians to reliably detect early signs of stroke, A novel approach was proposed to enhance the visual perception of ischemic stroke in non-enhanced CT.

Methods: A set of 39 retrospective CT scans were used, divided into 23 cases of acute ischemic stroke and 16 normal patients. Stroke cases were obtained within 4.

Classification of the trabecular bone structure of osteoporotic patients using machine vision.

Osteoporosis is a common bone disease which often leads to fractures. Clinically, the major challenge for the automatic diagnosis of osteoporosis is the complex architecture of bones. The clinical diagnosis of osteoporosis is conventionally done using Dual-energy X-ray Absorptiometry (DXA).

Subchondral tibial bone texture predicts the incidence of radiographic knee osteoarthritis: data from the Osteoarthritis Initiative.

Objectives: To evaluate whether trabecular bone texture (TBT) parameters measured on computed radiographs (CR) could predict the onset of radiographic knee osteoarthritis (OA).

Materials And Methods: Subjects from the Osteoarthritis Initiative (OAI) with no sign of radiographic OA at baseline were included. Cases that developed either a global radiographic OA defined by the Kellgren-Lawrence (KL) scale, a joint space narrowing (JSN) or tibial osteophytes (TOS) were compared with the controls with no changes after 48 months of follow-up.

Anisotropic Discrete Dual-Tree Wavelet Transform for Improved Classification of Trabecular Bone.

This paper deals with a new anisotropic discrete dual-tree wavelet transform (ADDTWT) to characterize the anisotropy of bone texture. More specifically, we propose to extend the conventional discrete dual-tree wavelet transform (DDTWT) by using the anisotropic basis functions associated with the hyperbolic wavelet transform instead of isotropic spectrum supports. A texture classification framework is adopted to assess the performance of the proposed transform.

Fractional Brownian Motion and Rao Geodesic Distance for Bone X-Ray Image Characterization.

Osteoporosis diagnosis has attracted particular attention in recent decades. Textured images from the microarchitecture of osteoporotic and healthy subjects show a high degree of similarity, increasing the difficulty of classifying such textures. Thus, the evaluation of osteoporosis from the bone X-ray images presents a major challenge for pattern recognition and medical applications.

Subchondral tibial bone texture analysis predicts knee osteoarthritis progression: data from the Osteoarthritis Initiative: Tibial bone texture & knee OA progression.

Objectives: To examine whether trabecular bone texture (TBT) parameters assessed on computed radiographs could predict knee osteoarthritis (OA) progression.

Methods: This study was performed using data from the Osteoarthritis Initiative (OAI). 1647 knees in 1124 patients had bilateral fixed flexion radiographs acquired 48 months apart.

Implications of the calf musculature and Achilles tendon architectures for understanding the site of injury.

Unlabelled: Clinically the sites of Achilles Tendon (AT) overuse conditions can be divided into the tendon mid-portion and osteotendinous attachment.

Purpose: We propose an anatomical analysis of the triceps surae musculotendon unit that could provide a possible anatomic explanation for these 2 sites of injury.

Method: Twelve cadavers (age 74±7 years) were studied.

Fracture discrimination by combined bone mineral density (BMD) and microarchitectural texture analysis.

The use of bone mineral density (BMD) for fracture discrimination may be improved by considering bone microarchitecture. Texture parameters such as trabecular bone score (TBS) or mean Hurst parameter (H) could help to find women who are at high risk of fracture in the non-osteoporotic group. The purpose of this study was to combine BMD and microarchitectural texture parameters (spine TBS and calcaneus H) for the detection of osteoporotic fractures.

Application of chemometric algorithms to MALDI mass spectrometry imaging of pharmaceutical tablets.

During drug product development, the nature and distribution of the active substance have to be controlled to ensure the correct activity and the safety of the final medication. Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), due to its structural and spatial specificities, provides an excellent way to analyze these two critical parameters in the same acquisition. The aim of this work is to demonstrate that MALDI-MSI, coupled with four well known multivariate statistical analysis algorithms (PCA, ICA, MCR-ALS and NMF), is a powerful technique to extract spatial and spectral information about chemical compounds from known or unknown solid drug product formulations.

Changes in prevalence of calcaneal spurs in men & women: a random population from a trauma clinic.

Background: This study reports the changing prevalence of ankle (Achilles and plantar) spurs with age, in order to comment on their significance to rheumatologists.

Methods: 1080 lateral ankle radiographs from each of 9 (50 men and 50 women) age cohorts from 2 to 96 years old of patients attending a trauma clinic were examined and spurs classified as small or large.

Results: The prevalence of both Achilles and plantar spurs in relation to the age categories and sex was variable.

Assessment of bone mineral density and radiographic texture analysis at the tibial subchondral bone.

Microstructural changes of subchondral bone constitute one of the figures characterising osteoarthritis on a structural level. Subchondral bone mineral density may reflect the complex relationship between bone and cartilage submitted to movement and loading. In this review, the authors discussed the interest of tibial subchondral bone mineral density assessment in the perspective of its diagnostic, etiopathogenic and prognostic value in osteoarthritis.

3D shape-dependent thinning method for trabecular bone characterization.

Purpose: Curve and surface thinning are widely-used skeletonization techniques for modeling objects in three dimensions. In the case of trabecular bone analysis, however, neither curve nor surface thinning is really efficient since the internal geometry of the object is usually composed of both rod and plate shapes. The purpose of this paper is to propose an original method called hybrid skeleton which better matches the geometry of the data compared to curve and surface skeletons.

Modeling of biological doses and mechanical effects on bone transduction.

Shear stress, hormones like parathyroid and mineral elements like calcium mediate the amplitude of stimulus signal, which affects the rate of bone remodeling. The current study investigates the theoretical effects of different metabolic doses in stimulus signal level on bone. The model was built considering the osteocyte as the sensing center mediated by coupled mechanical shear stress and some biological factors.

Genetic algorithm and image processing for osteoporosis diagnosis.

Osteoporosis is considered as a major public health threat. It is characterized by a decrease in the density of bone, decreasing its strength and leading to an increased risk of fracture. In this work, the morphological, topological and mechanical characteristics of 2 populations of arthritic and osteoporotic trabecular bone samples are evaluated using artificial intelligence and recently developed skeletonization algorithms.