Deep learning based detection of osteophytes in radiographs and magnetic resonance imagings of the knee using 2D and 3D morphology.

In this study, we investigated the discriminative capacity of knee morphology in automatic detection of osteophytes defined by the Osteoarthritis Research Society International atlas, using X-ray and magnetic resonance imaging (MRI) data. For the X-ray analysis, we developed a deep learning (DL) based model to segment femur and tibia. In case of MRIs, we utilized previously validated segmentations of femur, tibia, corresponding cartilage tissues, and menisci. Osteophyte detection was performed using DL models in four compartments: medial femur (FM), lateral femur (FL), medial tibia (TM), and lateral tibia (TL). To analyze the confounding effects of soft tissues, we investigated their morphology in combination with bones, including bones+cartilage, bones+menisci, and all the tissues. From X-ray-based 2D morphology, the models yielded balanced accuracy of 0.73, 0.69, 0.74, and 0.74 for FM, FL, TM, TL, respectively. Using 3D bone morphology from MRI, balanced accuracy was 0.80, 0.77, 0.71, and 0.76, respectively. The performance was higher than in 2D for all the compartments except for TM, with significant improvements observed for femoral compartments. Adding menisci or cartilage morphology consistently improved balanced accuracy in TM, with the greatest improvement seen for small osteophyte. Otherwise, the models performed similarly to bones-only. Our experiments demonstrated that MRI-based models show higher detection capability than X-ray based models for identifying knee osteophytes. This study highlighted the feasibility of automated osteophyte detection from X-ray and MRI data and suggested further need for development of osteophyte assessment criteria in addition to OARSI, particularly, for early osteophytic changes.

Semiautomatic Assessment of Facet Tropism From Lumbar Spine MRI Using Deep Learning: A Northern Finland Birth Cohort Study.

STUDY DESIGN: This is a retrospective, cross-sectional, population-based study that automatically measured the facet joint (FJ) angles from T2-weighted axial magnetic resonance imagings (MRIs) of the lumbar spine using deep learning (DL). OBJECTIVE: This work aimed to introduce a semiautomatic framework that measures the FJ angles using DL and study facet tropism (FT) in a large Finnish population-based cohort. SUMMARY OF DATA: T2-weighted axial MRIs of the lumbar spine (L3/4 through L5/S1) for (n=1288) in the NFBC1966 Finnish population-based cohort were used for this study. MATERIALS AND METHODS: A DL model was developed and trained on 430 participants' MRI images. The authors computed FJ angles from the model's prediction for each level, that is, L3/4 through L5/S1, for the male and female subgroups. Inter-rater and intrarater reliability was analyzed for 60 participants using annotations made by two radiologists and a musculoskeletal researcher. With the developed method, we examined FT in the entire NFBC1966 cohort, adopting the literature definitions of FT thresholds at 7° and 10°. The rater agreement was evaluated both for the annotations and the FJ angles computed based on the annotations. FJ asymmetry ( - was used to evaluate the agreement and correlation between the raters. Bland-Altman analysis was used to assess the agreement and systemic bias in the FJ asymmetry. The authors used the Dice score as the metric to compare the annotations between the raters. The authors evaluated the model predictions on the independent test set and compared them against the ground truth annotations. RESULTS: This model scored Dice (92.7±0.1) and intersection over union (87.1±0.2) aggregated across all the regions of interest, that is, vertebral body (VB), FJs, and posterior arch (PA). The mean FJ angles measured for the male and female subgroups were in agreement with the literature findings. Intrarater reliability was high, with a Dice score of VB (97.3), FJ (82.5), and PA (90.3). The inter-rater reliability was better between the radiologists with a Dice score of VB (96.4), FJ (75.5), and PA (85.8) than between the radiologists and the musculoskeletal researcher. The prevalence of FT was higher in the male subgroup, with L4/5 found to be the most affected region. CONCLUSION: The authors developed a DL-based framework that enabled us to study FT in a large cohort. Using the proposed method, the authors present the prevalence of FT in a Finnish population-based cohort.

Predicting osteoarthritis onset and progression with 3D texture analysis of cartilage MRI DESS: 6-Year data from osteoarthritis initiative.

In this study, we developed a gray level co-occurrence matrix-based 3D texture analysis method for dual-echo steady-state (DESS) magnetic resonance (MR) images to be used for knee cartilage analysis in osteoarthritis (OA) studies and use it to study changes in articular cartilage between different subpopulations based on their rate of progression into radiographically confirmed OA. In total, 642 series of right knee DESS MR images at 3T were obtained from baseline, 36- and 72-month follow-ups from the OA Initiative database. At baseline, all 214 subjects included in the study had Kellgren-Lawrence (KL) grade <2. Three groups were defined, based on time of progression into radiographic OA (ROA) (KL grades ≥2): control (no progression), fast progressor (ROA at 36 months), and slow progressor (ROA at 72 months) groups. 3D texture analysis was used to extract textural features for femoral and tibial cartilages. All textural features, in both femur and tibia, showed significant longitudinal changes across all groups and tissue layers. Most of the longitudinal changes were observed in progressors, but significant changes were observed also in controls. Differences between groups were mostly seen at baseline and 72 months. The method is sensitive to cartilage changes before and after ROA. It was able to detect longitudinal changes in controls and progressors and to distinguish cartilage alterations due to OA and aging. Moreover, it was able to distinguish controls and different progressor groups before any radiographic signs of OA and during OA. Thus, texture analysis could be used as a marker for the onset and progression of OA.

Deep learning-based segmentation of knee MRI for fully automatic subregional morphological assessment of cartilage tissues: Data from the Osteoarthritis Initiative.

Morphological changes in knee cartilage subregions are valuable imaging-based biomarkers for understanding progression of osteoarthritis, and they are typically detected from magnetic resonance imaging (MRI). So far, accurate segmentation of cartilage has been done manually. Deep learning approaches show high promise in automating the task; however, they lack clinically relevant evaluation. We introduce a fully automatic method for segmentation and subregional assessment of articular cartilage, and evaluate its predictive power in context of radiographic osteoarthritis progression. Two data sets of 3D double-echo steady-state (DESS) MRI derived from the Osteoarthritis Initiative were used: first, n = 88; second, n = 600, 0-/12-/24-month visits. Our method performed deep learning-based segmentation of knee cartilage tissues, their subregional division via multi-atlas registration, and extraction of subregional volume and thickness. The segmentation model was developed and assessed on the first data set. Subsequently, on the second data set, the morphological measurements from our and the prior methods were analyzed in correlation and agreement, and, eventually, by their discriminative power of radiographic osteoarthritis progression over 12 and 24 months, retrospectively. The segmentation model showed very high correlation (r > 0.934) and agreement (mean difference < 116 mm3 ) in volumetric measurements with the reference segmentations. Comparison of our and manual segmentation methods yielded r = 0.845-0.973 and mean differences = 262-501 mm3 for weight-bearing cartilage volume, and r = 0.770-0.962 and mean differences = 0.513-1.138 mm for subregional cartilage thickness. With regard to osteoarthritis progression, our method found most of the significant associations identified using the manual segmentation method, for both 12- and 24-month subregional cartilage changes. The method may be effectively applied in osteoarthritis progression studies to extract cartilage-related imaging biomarkers.

Automated analysis of rabbit knee calcified cartilage morphology using micro-computed tomography and deep learning.

Structural dynamics of calcified cartilage (CC) are poorly understood. Conventionally, CC structure is analyzed using histological sections. Micro-computed tomography (µCT) allows for three-dimensional (3D) imaging of mineralized tissues; however, the segmentation between bone and mineralized cartilage is challenging. Here, we present state-of-the-art deep learning segmentation for µCT images to assess 3D CC morphology. The sample includes 16 knees from 12 New Zealand White rabbits dissected into osteochondral samples from six anatomical regions: lateral and medial femoral condyles, lateral and medial tibial plateaus, femoral groove, and patella (n = 96). The samples were imaged with µCT and processed for conventional histology. Manually segmented CC from the images was used to train segmentation models with different encoder-decoder architectures. The models with the greatest out-of-fold evaluation Dice score were selected. CC thickness was compared across 24 regions, co-registered between the imaging modalities using Pearson correlation and Bland-Altman analyses. Finally, the anatomical CC thickness variation was assessed via a Linear Mixed Model analysis. The best segmentation models yielded average Dice of 0.891 and 0.807 for histology and µCT segmentation, respectively. The correlation between the co-registered regions was strong (r = 0.897, bias = 21.9 µm, standard deviation = 21.5 µm). Finally, both methods could separate the CC thickness between the patella, femoral, and tibial regions (p < 0.001). As a conclusion, the proposed µCT analysis allows for ex vivo 3D assessment of CC morphology. We demonstrated the biomedical relevance of the method by quantifying CC thickness in different anatomical regions with a varying mean thickness. CC was thickest in the patella and thinnest in the tibial plateau. Our method is relatively straightforward to implement into standard µCT analysis pipelines, allowing the analysis of CC morphology. In future research, µCT imaging might be preferable to histology, especially when analyzing dynamic changes in cartilage mineralization. It could also provide further understanding of 3D morphological changes that may occur in mineralized cartilage, such as thickening of the subchondral plate in osteoarthritis and other joint diseases.