Early detection of knee osteoarthritis using deep learning on knee magnetic resonance images

The aim of this study was to investigate the influence of MRI and patient data on the prediction of knee osteoarthritis (OA) incidence using different deep learning architectures. Knee OA incidence within 24 months was predicted using the intermediate-weighted turbo spin-echo (IW-TSE) sequence of 593 patients from the Osteoarthritis Initiative. To extract a region of interest containing the knee joint from the IW-TSE sequence, a U-Net model was trained and used to segment bone on a dual echo steady state (DESS) sequence. Subsequently, IW-TSE and DESS sequences were registered and the DESS segmentations were transformed to the corresponding IW-TSE scans. The performance of MRI-based features in the prediction of knee OA incidence was tested using three different deep learning architectures: a residual network (ResNet), a densely connected convolutional network (DenseNet), and a convolutional variational autoencoder (CVAE). To evaluate the predictive performance of MRI-based features alone, the outputs of ResNet, DenseNet, and CVAE were coupled with patient data (i.e., age, gender, BMI) and used as input to a Logistic Regression (LR) Classifier. Knee OA was defined based on visual MRI and X-ray-based OA features. The ResNet and DenseNet showed similar results, with both methods having the area under the receiver operating characteristic curve (AUC) values up to 0.6269. The best performing OA detection model was CVAE with an AUC of 0.6699 when combined with patient data and an AUC of 0.6689 when used alone as input to the LR classifier. The results showed that three deep learning algorithms have similar metrics when using IW-TSE MRIs and their performance increased with the inclusion of patient data, which shows the strong influence of variables such as age, gender, and BMI on the detection of knee OA

Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis

The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with and without radiographic osteoarthritis (OA). Bilateral posterior-anterior knee radiographs were analyzed from the baseline of OAI and MOST datasets. A fully automatic method to locate the most informative region from subchondral bone using adaptive segmentation was developed. We used an oversegmentation strategy for partitioning knee images into the compact regions that follow natural texture boundaries. LBP, Fractal Dimension (FD), Haralick features, Shannon entropy, and HOG methods were computed within the standard ROI and within the proposed adaptive ROIs. Subsequently, we built logistic regression models to identify and compare the performances of each texture descriptor and each ROI placement method using 5-fold cross validation setting. Importantly, we also investigated the generalizability of our approach by training the models on OAI and testing them on MOST dataset.We used area under the receiver operating characteristic (ROC) curve (AUC) and average precision (AP) obtained from the precision-recall (PR) curve to compare the results. We found that the adaptive ROI improves the classification performance (OA vs. non-OA) over the commonly used standard ROI (up to 9% percent increase in AUC). We also observed that, from all texture parameters, LBP yielded the best performance in all settings with the best AUC of 0.840 [0.825, 0.852] and associated AP of 0.804 [0.786, 0.820]. Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA

Novel X-ray-based methods for diagnostics of osteoarthritis

Abstract Osteoarthritis (OA) is the commonest joint disease in the world, and it has a major socioeconomic impact. OA causes progressive degenerative changes in the composition and structure of articular cartilage and subchondral bone. Clinical diagnosis of OA is based on physical examination and qualitative evaluation of changes on plain radiographs. Current clinical imaging methods are subjective or insensitive to early OA changes. Therefore, new methods are needed so as to quantify composition of the cartilage and characteristics of the subchondral bone. The aim of this thesis was to evaluate the potential of clinically applicable X-ray-based methods for the assessment of the cartilage proteoglycan content as well as the structure and density of subchondral bone in a knee joint. Subchondral bone density and structure (local binary patterns, Laplacian, and fractal-based algorithms) analysis methods for two-dimensional (2-D) plain radiographs were validated against three-dimensional (3-D) bone microarchitecture obtained from micro-computed tomography ex vivo and applied to plain radiographs in vivo. Furthermore, a method developed for the evaluation of articular cartilage proteoglycan content from computed tomography (CT) was validated against a delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC), which is widely used as a proteoglycan sensitive method, in subjects referred for an arthroscopy of the knee joint. Subchondral bone density and structure evaluated from 2-D radiographs were significantly related to the bone volume fraction and true 3-D microarchitecture of bone, respectively. In addition, bone density- and structure-related parameters from radiographs were significantly different among subjects with different stages of OA. Cartilage proteoglycan content evaluated from CT was significantly related to dGEMRIC method. Furthermore, dGEMRIC was associated with bone structure from a 2-D radiograph. In conclusion, analysis of bone structure and density is feasible from clinically available 2-D radiographs. A novel CT method sensitive to proteoglycan content should be considered when a 3-D view of cartilage quality is needed.Tiivistelmä Nivelrikko on maailman yleisin nivelsairaus. Se aiheuttaa merkittävää kärsimystä potilaille, ja sillä on suuri taloudellinen vaikutus yhteiskuntaan. Nivelrikko aiheuttaa palautumattomia muutoksia nivelrustokudoksen ja rustonalaisen luun koostumukseen ja rakenteeseen. Nivelrikon diagnoosi perustuu kliiniseen tutkimukseen ja röntgenkuvien silmämääräiseen arviointiin. Nykyiset nivelrikon kliiniset kuvantamismenetelmät ovat subjektiivisia eivätkä riittävän tarkkoja nivelrikon varhaisten muutosten osoittamiseen, minkä vuoksi rustokudoksen koostumuksen ja rustonalaisen luun muutosten arviointiin tarvitaan uusia menetelmiä. Tämän väitöskirjantyön tarkoituksena oli tutkia uusien röntgensäteilyyn perustuvien menetelmien soveltuvuutta polvinivelen rustokudoksen proteoglykaanipitoisuuden sekä luun tiheyden ja rakenteen arviointiin. Rustonalaisen luun tiheyttä ja rakennetta arvioitiin digitaalisesta röntgenkuvasta tietokonepohjaisilla menetelmillä ja tuloksia verrattiin mikrotietokonetomografiassa nähtävään luun kolmiulotteiseen rakenteeseen. Röntgenkuvasta laskettavia muuttujia verrattiin myös eriasteisesta nivelrikosta kärsivien henkilöiden välillä. Rustokudoksen proteoglykaanipitoisuutta epäsuorasti mittaavaa tietokonetomografiamenetelmää verrattiin vastaavaan magneettikuvausmenetelmään henkilöillä, jotka olivat menossa polven niveltähystykseen. Röntgenkuvasta laskettu rustonalaisen luun tiheys ja rakenne olivat tilastollisesti selkeästi yhteydessä luun tilavuusmäärään ja mikrorakenteeseen, ja ne erosivat eriasteisesta nivelrikosta kärsivien henkilöiden välillä. Proteoglykaanipitoisuutta arvioivien tietokonetomografia- ja magneettikuvausmenetelmien välillä oli tilastollisesti merkitsevä korrelaatio. Ruston proteoglykaanipitoisuutta arvioivan magneettikuvausmenetelmän ja röntgenkuvasta laskettavan luun rakenteen välillä oli myös tilastollinen yhteys. Loppupäätelmänä voidaan todeta, että luun tiheyttä ja rakennetta on mahdollista arvioida kliinisesti saatavilla olevista röntgenkuvista. Tietokonetomografiamenetelmän käyttöä tulee harkita tutkimuksissa silloin, kun rustokudoksen tilasta halutaan kolmiulotteista tietoa

Ultrasound arthroscopy of human knee cartilage and subchondral bone in vivo

Arthroscopic ultrasound imaging enables quantitative evaluation of articular cartilage. However, the potential of this technique for evaluation of subchondral bone has not been investigated in vivo. In this study, we address this issue in clinical arthroscopy of the human knee (n = 11) by determining quantitative ultrasound (9 MHz) reflection and backscattering parameters for cartilage and subchondral bone. Furthermore, in each knee, seven anatomical sites were graded using the International Cartilage Repair Society (ICRS) system based on (i) conventional arthroscopy and (ii) ultrasound images acquired in arthroscopy with a miniature transducer. Ultrasound enabled visualization of articular cartilage and subchondral bone. ICRS grades based on ultrasound images were higher (p < 0.05) than those based on conventional arthroscopy. The higher ultrasound-based ICRS grades were expected as ultrasound reveals additional information on, for example, the relative depth of the lesion. In line with previous literature, ultrasound reflection and scattering in cartilage varied significantly (p < 0.05) along the ICRS scale. However, no significant correlation between ultrasound parameters and structure or density of subchondral bone could be demonstrated. To conclude, arthroscopic ultrasound imaging had a significant effect on clinical grading of cartilage, and it was found to provide quantitative information on cartilage. The lack of correlation between the ultrasound parameters and bone properties may be related to lesser bone change or excessive attenuation in overlying cartilage and insufficient power of the applied miniature transducer

Bone density and texture from minimally post-processed knee radiographs in subjects with knee osteoarthritis

Abstract Plain radiography is the most common modality to assess the stage of osteoarthritis. Our aims were to assess the relationship of radiography-based bone density and texture between radiographs with minimal and clinical post-processing, and to compare the differences in bone characteristics between controls and subjects with knee osteoarthritis or medial tibial bone marrow lesions (BMLs). Tibial bone density and texture was evaluated from radiographs with both minimal and clinical post-processing in 109 subjects with and without osteoarthritis. Bone texture was evaluated using fractal signature analysis. Significant correlations (p &lt; 0.001) were found in all regions (between 0.94 and 0.97) for calibrated bone density between radiographs with minimal and clinical post-processing. Correlations varied between 0.51 and 0.97 (p &lt;0.001) for FDVer texture parameter and between −0.10 and 0.97 for FDHor. Bone density and texture were different (p &lt; 0.05) between controls and subjects with osteoarthritis or BMLs mainly in medial tibial regions. When classifying healthy and osteoarthritic subjects using a machine learning-based elastic net model with bone characteristics, area under the receiver operating characteristics (ROCAUC) curve was 0.77. For classifying controls and subjects with BMLs, ROCAUC was 0.85. In conclusion, differences in bone density and texture can be assessed from knee radiographs when using minimal post-processing

Discrimination of Low-Energy Acetabular Fractures from Controls Using Computed Tomography-Based Bone Characteristics

The incidence of low-energy acetabular fractures has increased. However, the structural factors for these fractures remain unclear. The objective of this study was to extract trabecular bone architecture and proximal femur geometry (PFG) measures from clinical computed tomography (CT) images to (1) identify possible structural risk factors of acetabular fractures, and (2) to discriminate fracture cases from controls using machine learning methods. CT images of 107 acetabular fracture subjects (25 females, 82 males) and 107 age-gender matched controls were examined. Three volumes of interest, one at the acetabulum and two at the femoral head, were extracted to calculate bone volume fraction (BV/TV), gray-level co-occurrence matrix and histogram of the gray values (GV). The PFG was defined by neck shaft angle and femoral neck axis length. Relationships between the variables were assessed by statistical mean comparisons and correlation analyses. Bayesian logistic regression and Elastic net machine learning models were implemented for classification. We found lower BV/TV at the femoral head (0.51 vs. 0.55, p = 0.012) and lower mean GV at both the acetabulum (98.81 vs. 115.33, p < 0.001) and femoral head (150.63 vs. 163.47, p = 0.005) of fracture subjects when compared to their matched controls. The trabeculae within the femoral heads of the acetabular fracture sides differed in structure, density and texture from the corresponding control sides of the fracture subjects. Moreover, the PFG and trabecular architectural variables, alone and in combination, were able to discriminate fracture cases from controls (area under the receiver operating characteristics curve 0.70 to 0.79). In conclusion, lower density in the acetabulum and femoral head with abnormal trabecular structure and texture at the femoral head, appear to be risk factors for low-energy acetabular fractures

A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone

Objectives: Our aim was to assess the ability of semi-automatically extracted magnetic resonance imaging (MRI)–based radiomic features from tibial subchondral bone to distinguish between knees without and with osteoarthritis. Methods: The right knees of 665 females from the population-based Rotterdam Study scanned with 1.5T MRI were analyzed. A fast imaging employing steady-state acquisition sequence was used for the quantitative bone analyses. Tibial bone was segmented using a method that combines multi-atlas and appearance models. Radiomic features related to the shape and texture were calculated from six volumes of interests (VOIs) in the proximal tibia. Machine learning–based Elastic Net models with 10-fold cross-validation were used to distinguish between knees without and with MRI Osteoarthritis Knee Score (MOAKS)–based tibiofemoral osteoarthritis. Performance of the covariate (age and body mass index), image features, and combined covariate + image features models were assessed using the area under the receiver operating characteristic curve (ROC AUC). Results: Of 665 analyzed knees, 76 (11.4%) had osteoarthritis. An ROC AUC of 0.68 (95% confidence interval (CI): 0.60–0.75) was obtained using the covariate model. The image features model yielded an ROC AUC of 0.80 (CI: 0.73–0.87). The model that combined image features from all VOIs and covariates yielded an ROC AUC of 0.80 (CI: 0.73–0.87). Conclusion: Our results suggest that radiomic features are useful imaging biomarkers of subchondral bone for the diagnosis of osteoarthritis. An advantage of assessing bone on MRI instead of on radiographs is that other tissues can be assessed simultaneously. Key Points: • Subchondral bone plays a role in the osteoarthritis disease processes. • MRI radiomics is a potential method for quantifying changes in subchondral bone. • Semi-automatically extracted radiomic features of tibia differ between subjects without and with osteoarthritis

Volumetric assessment of bone microstructures by a 3D local binary patterns -based method:bone changes with osteoarthritis

Abstract Osteoarthritis (OA) causes progressive degeneration of articular cartilage and pathological changes in subchondral bone, conventionally assessed volumetrically using micro-computed tomography (μCT) imaging in vitro. The local binary patterns (LBP) method has recently been suggested as a new alternative solution to perform analysis of local bone structures from μCT scans. In this study, a novel 3D LBP-based method to provide a new lead in bone microstructural analysis is proposed. In addition to the detailed description of the method, this solution is tested using µCT data of OA human trabecular bone samples, harvested from patients treated with total knee arthroplasty. The method was applied to correlate the distribution of orientations of local patterns with the severity of the disease. The local orientations of the bone fibers changed along the severity of OA, suggesting an adaptation of the bone to the disease. The structural parameters derived from the process were able to provide a new approach for the assessment of the disease, supporting the potential of this volumetric LBP-based method to assess trabecular bone changes

Discrimination of low-energy acetabular fractures from controls using computed tomography-based bone characteristics

Abstract The incidence of low-energy acetabular fractures has increased. However, the structural factors for these fractures remain unclear. The objective of this study was to extract trabecular bone architecture and proximal femur geometry (PFG) measures from clinical computed tomography (CT) images to (1) identify possible structural risk factors of acetabular fractures, and (2) to discriminate fracture cases from controls using machine learning methods. CT images of 107 acetabular fracture subjects (25 females, 82 males) and 107 age-gender matched controls were examined. Three volumes of interest, one at the acetabulum and two at the femoral head, were extracted to calculate bone volume fraction (BV/TV), gray-level co-occurrence matrix and histogram of the gray values (GV). The PFG was defined by neck shaft angle and femoral neck axis length. Relationships between the variables were assessed by statistical mean comparisons and correlation analyses. Bayesian logistic regression and Elastic net machine learning models were implemented for classification. We found lower BV/TV at the femoral head (0.51 vs. 0.55, p = 0.012) and lower mean GV at both the acetabulum (98.81 vs. 115.33, p &lt; 0.001) and femoral head (150.63 vs. 163.47, p = 0.005) of fracture subjects when compared to their matched controls. The trabeculae within the femoral heads of the acetabular fracture sides differed in structure, density and texture from the corresponding control sides of the fracture subjects. Moreover, the PFG and trabecular architectural variables, alone and in combination, were able to discriminate fracture cases from controls (area under the receiver operating characteristics curve 0.70 to 0.79). In conclusion, lower density in the acetabulum and femoral head with abnormal trabecular structure and texture at the femoral head, appear to be risk factors for low-energy acetabular fractures