Clinical validation of the use of prototype software for automatic cartilage segmentation to quantify knee cartilage in volunteers

Background The cartilage segmentation algorithms make it possible to accurately evaluate the morphology and degeneration of cartilage. There are some factors (location of cartilage subregions, hydrarthrosis and cartilage degeneration) that may influence the accuracy of segmentation. It is valuable to evaluate and compare the accuracy and clinical value of volume and mean T2* values generated directly from automatic knee cartilage segmentation with those from manually corrected results using prototype software. Method Thirty-two volunteers were recruited, all of whom underwent right knee magnetic resonance imaging examinations. Morphological images were obtained using a three-dimensional (3D) high-resolution Double-Echo in Steady-State (DESS) sequence, and biochemical images were obtained using a two-dimensional T2* mapping sequence. Cartilage score criteria ranged from 0 to 2 and were obtained using the Whole-Organ Magnetic Resonance Imaging Score (WORMS). The femoral, patellar, and tibial cartilages were automatically segmented and divided into subregions using the post-processing prototype software. Afterwards, all the subregions were carefully checked and manual corrections were done where needed. The dice coefficient correlations for each subregion by the automatic segmentation were calculated. Results Cartilage volume after applying the manual correction was significantly lower than automatic segmentation (P < 0.05). The percentages of the cartilage volume change for each subregion after manual correction were all smaller than 5%. In all the subregions, the mean T2* relaxation time within manual corrected subregions was significantly lower than in regions after automatic segmentation (P < 0.05). The average time for the automatic segmentation of the whole knee was around 6 min, while the average time for manual correction of the whole knee was around 27 min. Conclusions Automatic segmentation of cartilage volume has a high dice coefficient correlation and it can provide accurate quantitative information about cartilage efficiently without individual bias. Advances in knowledge: Magnetic resonance imaging is the most promising method to detect structural changes in cartilage tissue. Unfortunately, due to the structure and morphology of the cartilages obtaining accurate segmentations can be problematic. There are some factors (location of cartilage subregions, hydrarthrosis and cartilage degeneration) that may influence segmentation accuracy. We therefore assessed the factors that influence segmentations error.

Patellar cartilage increase following ACL reconstruction with and without meniscal pathology: a two-year prospective MRI morphological study

Background Anterior cruciate ligament reconstruction (ACLR) together with concomitant meniscal injury are risk factors for the development of tibiofemoral (TF) osteoarthritis (OA), but the potential effect on the patellofemoral (PF) joint is unclear. The aim of this study was to: (i) investigate change in patellar cartilage morphology in individuals 2.5 to 4.5 years after ACLR with or without concomitant meniscal pathology and in healthy controls, and (ii) examine the association between baseline patellar cartilage defects and patellar cartilage volume change. Methods Thirty two isolated ACLR participants, 25 ACLR participants with combined meniscal pathology and nine healthy controls underwent knee magnetic resonance imaging (MRI) with 2-year intervals (baseline = 2.5 years post-ACLR). Patellar cartilage volume and cartilage defects were assessed from MRI using validated methods. Results Both ACLR groups showed patellar cartilage volume increased over 2 years (p < 0.05), and isolated ACLR group had greater annual percentage cartilage volume increase compared with controls (mean difference 3.6, 95% confidence interval (CI) 1.0, 6.3%, p = 0.008) and combined ACLR group (mean difference 2.2, 95% CI 0.2, 4.2%, p = 0.028). Patellar cartilage defects regressed in the isolated ACLR group over 2 years (p = 0.02; Z = − 2.33; r = 0.3). Baseline patellar cartilage defect score was positively associated with annual percentage cartilage volume increase (Regression coefficient B = 0.014; 95% CI 0.001, 0.027; p = 0.03) in the pooled ACLR participants. Conclusions Hypertrophic response was evident in the patellar cartilage of ACLR participants with and without meniscal pathology. Surprisingly, the increase in patellar cartilage volume was more pronounced in those with isolated ACLR. Although cartilage defects stabilised in the majority of ACLR participants, the severity of patellar cartilage defects at baseline influenced the magnitude of the cartilage hypertrophic response over the subsequent ~ 2 years.

Towards novel osteoarthritis biomarkers: Multi-criteria evaluation of 46,996 segmented knee MRI data from the Osteoarthritis Initiative

Convolutional neural networks (CNNs) are the state-of-the-art for automated assessment of knee osteoarthritis (KOA) from medical image data. However, these methods lack interpretability, mainly focus on image texture, and cannot completely grasp the analyzed anatomies’ shapes. In this study we assess the informative value of quantitative features derived from segmentations in order to assess their potential as an alternative or extension to CNN-based approaches regarding multiple aspects of KOA. Six anatomical structures around the knee (femoral and tibial bones, femoral and tibial cartilages, and both menisci) are segmented in 46,996 MRI scans. Based on these segmentations, quantitative features are computed, i.e., measurements such as cartilage volume, meniscal extrusion and tibial coverage, as well as geometric features based on a statistical shape encoding of the anatomies. The feature quality is assessed by investigating their association to the Kellgren-Lawrence grade (KLG), joint space narrowing (JSN), incident KOA, and total knee replacement (TKR). Using gold standard labels from the Osteoarthritis Initiative database the balanced accuracy (BA), the area under the Receiver Operating Characteristic curve (AUC), and weighted kappa statistics are evaluated. Features based on shape encodings of femur, tibia, and menisci plus the performed measurements showed most potential as KOA biomarkers. Differentiation between non-arthritic and severely arthritic knees yielded BAs of up to 99%, 84% were achieved for diagnosis of early KOA. Weighted kappa values of 0.73, 0.72, and 0.78 were achieved for classification of the grade of medial JSN, lateral JSN, and KLG, respectively. The AUC was 0.61 and 0.76 for prediction of incident KOA and TKR within one year, respectively. Quantitative features from automated segmentations provide novel biomarkers for KLG and JSN classification and show potential for incident KOA and TKR prediction. The validity of these features should be further evaluated, especially as extensions of CNN-based approaches. To foster such developments we make all segmentations publicly available together with this publication.

Stem Cells in Autologous Microfragmented Adipose Tissue: Current Perspectives in Osteoarthritis Disease

Osteoarthritis (OA) is a chronic debilitating disorder causing pain and gradual degeneration of weight-bearing joints with detrimental effects on cartilage volume as well as cartilage damage, generating inflammation in the joint structure. The etiology of OA is multifactorial. Currently, therapies are mainly addressing the physical and occupational aspects of osteoarthritis using pharmacologic pain treatment and/or surgery to manage the symptomatology of the disease with no specific regard to disease progression or prevention. Herein, we highlight alternative therapeutics for OA specifically considering innovative and encouraging translational methods with the use of adipose mesenchymal stem cells.

A generalized deep learning framework for automatic rheumatoid arthritis severity grading

Knee rheumatoid arthritis (RA) is the highly prevalent, chronic, progressive condition in the world. To diagnose this disease in the early stage in detail analysis with magnetic resonance (MR) image is possible. The imaging modality feature allows unbiased assessment of joint space narrowing (JSN), cartilage volume, and other vital features. This provides a fine-grained RA severity evaluation of the knee, contrasted to the benchmark, and generally used Kellgren Lawrence (KL) assessment. In this research, an intelligent system is developed to predict KL grade from the knee dataset. Our approach is based on hybrid deep learning of 50 layers (ResNet50) with skip connections. The proposed approach also uses Adam optimizer to provide learning linearity in the training stage. Our approach yields KL grade and JSN for femoral and tibial tissue with lateral and medial compartments. Furthermore, the approach also yields area under curve (AUC) of 0.98, accuracy 96.85%, mean absolute error (MAE) 0.015, precision 98.31%, and other commonly used parameters for the existence of radiographic RA progression which is improved than the existing state-of-the-art.

Associations Between the Morphological Parameters of Proximal Tibiofibular Joint (PTFJ) and Changes in Tibiofemoral Joint Structures in Patients with Knee Osteoarthritis

BackgroundTo describe the longitudinal associations between the morphological parameters of proximal tibiofibular joint (PTFJ) and joint structural changes in tibiofemoral compartments in patients with knee osteoarthritis (OA).MethodsThe participants were selected from the Vitamin D Effects on Osteoarthritis (VIDEO) study. PTFJ morphological parameters were measured on coronal and sagittal MRI. The contacting area (S) of PTFJ, and its projection areas onto the horizontal (load-bearing area, Sτ), sagittal (lateral stress-bolstering area, Sφ) and coronal plane (posterior stress-bolstering area, Sυ) were assessed. Knee structural abnormalities, including cartilage defects, bone marrow lesions (BMLs) and cartilage volume, were evaluated at baseline and after 2 years. Log binominal regression models and linear regression models were used to assess the associations between PTFJ morphological parameters and osteoarthritic structural changes.ResultsIn the longitudinal analyses, the S (RR: 1.45) and Sτ (RR: 1.55) of PTFJ were significantly and positively associated with an increase in medial tibial (MT) cartilage defects. The Sτ (β: -0.07), Sυ (β: -0.07), and S (β: -0.06) of PTFJ were significantly and negatively associated with changes in MT cartilage volume. The Sτ (RR: 1.55) of PTFJ was positively associated with an increase in MT BMLs, and Sφ (RR: 0.35) was negatively associated with an increase in medial femoral BMLs. ConclusionsThis longitudinal study suggests that higher load-bearing area of PTFJ could be a risk factor for structural changes in medial tibiofemoral (MTF) compartment in knee OA. This may provide a theoretical support for proximal fibular osteotomy in the treatment of MTF OA.Trial registration: Clinicaltrials.gov Identifier: NCT01176344; Anzctr.org.au Identifier: ACTRN12610000495022Date of registration: 7 May 2010

Gender and Sex Are Key Determinants in Osteoarthritis Not Only Confounding Variables. A Systematic Review of Clinical Data

Many risk factors for osteoarthritis (OA) have been noted, while gender/sex differences have been understated. The work aimed to systematically review literature investigating as primary aim the relationship between gender/sex related discriminants and OA. The search was performed in PubMed, Science Direct and Web of Knowledge in the last 10 years. Inclusion criteria were limited to clinical studies of patients affected by OA in any joints, analyzing as primary aim gender/sex differences. Exclusion criteria were review articles, in vitro, in vivo and ex vivo studies, case series studies and papers in which gender/sex differences were adjusted as confounding variable. Of the 120 records screened, 42 studies were included. Different clinical outcomes were analyzed: morphometric differences, followed by kinematics, pain, functional outcomes after arthroplasty and health care needs of patients. Women appear to use more health care, have higher OA prevalence, clinical pain and inflammation, decreased cartilage volume, physical difficulty, and smaller joint parameters and dimensions, as compared to men. No in-depth studies or mechanistic studies analyzing biomarker differential expressions, molecular pathways and omic profiles were found that might drive preclinical and clinical research towards sex-/gender-oriented protocols.