Biology subjects

Korhonen, R. K.

Publications and source records attributed to Korhonen, R. K..

14 recordsLinked to original sources

bioRxiv2026

A computational framework to model cartilage degeneration induced by mechanoinflammation and cytokine-driven inflammation in post-traumatic osteoarthritis

Knee joint injury is a major risk factor for post-traumatic osteoarthritis (PTOA), often associated with early cartilage degeneration. Mechanical overloading and cytokine-driven inflammation are key drivers of this process, yet the underlying mechanisms and their distinct temporal and spatial contributions to cartilage degradation remain unclear. Here, we present a mechanobiological finite element framework that simulates cartilage degradation through cell-mediated proteolytic activity triggered by mechanoinflammation and cytokine-driven inflammation. The model reproduces experimentally observed depth-dependent loss of collagen and aggrecan, with mechanoinflammation inducing a transient response and cytokine-driven inflammation sustaining prolonged matrix degradation. Sensitivity analysis further shows that mechanoinflammation-driven degradation is governed mainly by protease production per cell, whereas cytokine-driven degradation is more sensitive to the rate of cellular stimulation. Together, this framework provides a mechanistic basis to study proteolytic cartilage degeneration and supports future in silico evaluation of therapeutic strategies aimed at mitigating cartilage degradation in PTOA.

biophysics↗

bioRxiv2025

Pericellular Matrix Proteoglycan Content is Less Affected in The Mid-Zone Cartilage in Early Post-Traumatic Osteoarthritis

Proteoglycan content changes in articular cartilage during post-traumatic osteoarthritis affect both chondrocyte biomechanics and tissue health. Previously, we found that four weeks after anterior cruciate ligament transection (ACLT) in rabbits, proteoglycan loss was less pronounced in the pericellular matrix (PCM) than in the extracellular matrix (ECM), resulting in a greater PCM-to-ECM proteoglycan content ratio compared to controls. Here, we investigate whether this pattern is already present two weeks post-ACLT and whether changes in the proteoglycan ratio relate to cell volumetric strain in the superficial zone during tissue loading. Unilateral ACLT was performed in eight skeletally mature rabbits. Two weeks post-surgery, cartilage from all load-bearing sites of the operated and contralateral knees were collected. Age-matched, non-operated knees from separate animals served as controls. Proteoglycan content in the chondrocyte microenvironment was analyzed in the superficial and middle zones of cartilage via digital densitometry. Volumetric strain of superficial zone cells was measured using two-photon confocal microscopy during indentation. In the superficial zone, the PCM-to-ECM proteoglycan content ratio was greater in the ACLT knees than in controls for the lateral tibial plateau (p = 0.043). However, this was not clearly linked to changes in cell volumetric strain. Interestingly, in the middle zone, the PCM-to-ECM proteoglycan content ratio was greater in the ACLT knees than in controls for the medial femoral condyle, femoral groove, and patella (p < 0.05). These results suggest that, in early post-traumatic osteoarthritis, proteoglycan loss is less severe in the PCM than in the ECM, especially in the middle cartilage zone.

biophysics↗

bioRxiv2025

MMP release following cartilage injury leads to collagen loss in intact tissue - a computational study

Damage of collagen fibril network in articular cartilage plays a key role in post-traumatic osteoarthritis but the main underlying mechanobiological mechanisms of fibrils degeneration early after injury are not fully understood. This study explores the hypothesis that injurious loading leads to cellular damage that triggers the release of matrix metalloproteinases (MMPs), resulting in loss of collagen content in cartilage. To investigate this, we developed a computational mechano-signaling model simulating spatial collagen loss in bovine cartilage. In the model, the injurious loading causes excessive shear strains in tissue matrix, leading to cell damage and subsequent release of MMPs. The model was compared to ex vivo cartilage explant experiments over 12 days post-injury where collagen content was assessed via Fourier-transform infrared microspectroscpy. By day 12, the simulated collagen loss aligned with our experimental findings along most of tissue depth ([~]30% bulk average loss in the model vs. [~]35% in the experiment). The results suggest that injury-related cell damage and the downstream MMP activity could partly explain the depth-wise collagen content loss in early days after ex vivo cartilage injury. Ultimately, combining the current approach with joint-level computational models could enhance the prediction of the onset and progression of cartilage degeneration. Author SummaryKnee injuries can initiate an irreversible degeneration of articular cartilage which can later lead to the development of post-traumatic osteoarthritis over the years. Currently, there is no cure or effective intervention to repair degenerated cartilage or halt disease progression. Yet, opportunities for intervention depend on a thorough understanding of mechanisms that govern the very early changes in cartilage tissue composition such as the collagen fibrils. In this work, we developed a finite element computational modeling framework to simulate cartilage following injurious loading, focusing on the early loss of collagen content. The model aims to capture the role of injury-induced cellular damage in elevating proteolytic activity within the tissue, which could rapidly degrade collagen fibrils and result in significant collagen loss. This model framework offers a tool for studying the degradation of the collagen fibril network, testing hypotheses involving cell-driven mechano-signalling pathways and evaluating potential treatment interventions aimed at preventing collagen loss.

biophysics↗

bioRxiv2024

Time-dependent computational model of post-traumatic osteoarthritis to estimate how mechanoinflammatory mechanisms impact cartilage aggrecan content

Degenerative musculoskeletal diseases like osteoarthritis can be initiated by joint injury. Injurious overloading-induced mechanical straining of articular cartilage and subsequent biological responses may trigger cartilage degradation. One early sign of degradation is loss of aggrecan content which is potentially accelerated near chondral lesions under physiological loading. Yet, the mechanoinflammatory mechanisms explaining time-dependent degradation in regions with disparate mechanical loading are unclear and challenging to assess with experiments alone. Here, we developed computational models unraveling potential mechanisms behind aggrecan content adaptation. Incorporating mechanical strain-driven cell damage and downstream proteolytic enzyme release, fluid flow-driven aggrecan depletion, and fluid pressure-stimulated regulation of aggrecan biosynthesis, the models agreed with experiments and exhibited 14%-points greater near-lesion aggrecan loss after 12 days of physiological loading compared to without loading. This significant advancement in mechanistic understanding incorporated into cartilage adaptation model can help in development and guidance of personalized therapies, such as rehabilitation protocols and tissue- engineered constructs.

bioengineering↗

bioRxiv2024

Mechanobiochemical finite element model to analyze impact-loading-induced cell damage, subsequent proteoglycan loss, and anti-oxidative treatment effects in articular cartilage

Joint trauma often leads to articular cartilage degeneration and post-traumatic osteoarthritis (PTOA). Pivotal determinants include trauma-induced excessive tissue strains that damage cartilage cells. As a downstream effect, these damaged cells can trigger cartilage degeneration via oxidative stress, cell death, and proteolytic tissue degeneration. N-acetylcysteine (NAC) has emerged as antioxidant capable of inhibiting oxidative stress, cell death, and cartilage degeneration post-impact. However, temporal effects of NAC are not fully understood and remain difficult to assess solely by physical experiments. Thus, we developed a computational framework to simulate a drop tower impact of cartilage with finite element analysis in ABAQUS, and model subsequent oxidative stress-related cell damage, and NAC treatment upon cartilage proteoglycan content in COMSOL Multiphysics, based on prior ex vivo experiments. Model results provide evidence that by inhibiting further cell damage by mechanically induced oxidative stress, immediate NAC treatment can reduce proteoglycan loss by mitigating cell death (loss of proteoglycan biosynthesis) and enzymatic proteoglycan depletion. Our simulations also indicated that delayed NAC treatment may not inhibit cartilage proteoglycan loss despite reduced cell death after impact. These results enhance understanding of temporal effects of impact-related cell damage and treatment that are critical for the development of effective treatments for PTOA. In the future, our modeling framework could increase understanding of time-dependent mechanisms of oxidative stress and downstream effects in injured cartilage and aid in developing better treatments to mitigate PTOA progression. Author summaryPost-traumatic osteoarthritis is a debilitating disease which is often initiated by trauma and characterized by cartilage degeneration. The degeneration is partly driven by trauma-induced damage, including oxidative stress, to cartilage cells. Multiple drugs have been studied to counteract the cell damage, but it remains difficult to inhibit the disease progression since temporal effects of such treatments are not fully understood. Here, we developed a computational framework to study effects of antioxidant treatment in mechanically impacted cartilage and compared our computational simulations with previously published experiments. Our results showed that the high strain induced cell damage occurring in the mechanically impacted region could be mitigated by N-acetylcysteine treatment. This mechanism could partly explain reduced cartilage proteoglycan loss compared to untreated samples. Our modeling framework could help enhance development of treatments to better inhibit osteoarthritis progression.

biophysics↗

bioRxiv2024

Joint and tissue mechanics in post-traumatic osteoarthritis: insights from the rat model

ObjectiveAltered mechanical loading is a known risk factor for osteoarthritis. Destabilization of the medial meniscus (DMM) is a preclinical gold standard model for post-traumatic osteoarthritis and is thought to induce instability and locally increased loading. However, the joint- and tissue-level mechanical environment underlying cartilage degeneration remains poorly documented. DesignUsing a custom multiscale modeling approach, we assessed joint and tissue biomechanics in rats undergoing sham surgery and DMM. High-fidelity experimental gait data were collected in a setup combining biplanar fluoroscopy and a ground reaction force plate. Knee poses and joint-level loading were estimated through musculoskeletal modeling, using bony landmarks, semi-automatically tracked via deep learning on fluoroscopic images, and ground reaction forces. A musculoskeletal model of the rat hindlimb was adapted to represent knee flexion-extension, valgus-varus, and internal-external rotation. The tissue-level cartilage mechanical environment was then spatially estimated, using the musculoskeletal modeling parameters as inputs into a dedicated finite element (FE) model of the rat knee, comprising cartilage and meniscal tissues. Experimental gait data and modeling workflows, including musculoskeletal models and FE meshes, are openly shared through a data repository. ResultsIn rats with DMM, the frontal plane knee pose was altered, yet there was no indication of joint-level overloading. Tissue-level mechanical cues typically linked with cartilage degeneration were not increased in the medial tibial cartilage, despite evidence of tissue structural changes. ConclusionDMM did not increase joint and tissue mechanical responses in the knee medial compartment, suggesting that mechanical loading alone does not explain the observed osteoarthritis-like structural changes.

bioengineering↗

bioRxiv2024

Mechanical Drivers of Glycosaminoglycan Content Changes in Intact and Damaged Human Cartilage

Articular cartilage undergoes significant degeneration during osteoarthritis, currently lacking effective treatments. This study explores mechanical influences on cartilage health using a novel finite element-based mechanoregulatory model, predicting combined degenerative and regenerative responses to mechanical loading. Calibrated and validated through one-week longitudinal ex vivo experiments on intact and damaged cartilage samples, the model underscores the roles of maximum shear strain, fluid velocity, and dissipated energy in driving changes in cartilage glycosaminoglycan (GAG) content. It delineates the distinct regenerative contributions of fluid velocity and dissipated energy, alongside the degenerative contribution of maximum shear strain, to GAG adaptation in both intact and damaged cartilage under physiological mechanical loading. Remarkably, the model predicts increased GAG production even in damaged cartilage, consistent with our in vitro experimental findings. Beyond advancing our understanding of mechanical loadings role in cartilage homeostasis, our model aligns with contemporary ambitions by exploring the potential of in silico trials to optimize mechanical loading in degenerative joint disease, fostering personalized rehabilitation.

bioengineering↗

bioRxiv2023

An Automated and Robust Tool for Musculoskeletal and Finite Element Modeling of the Knee Joint

ObjectiveTo develop and assess an automatic and robust knee musculoskeletal finite element (MSK-FE) modeling pipeline. MethodsMagnetic resonance images (MRI) were used to train nnU-Net networks for auto-segmentation of knee bones (femur, tibia, patella, and fibula), cartilages (femur, tibia, and patella), menisci, and major knee ligaments. Two different MRI sequences were used to broaden applicability. Next, we created MSK-FE models of an unseen dataset using two MSK-FE modeling pipelines: template-based and auto-meshing. MSK models had personalized knee geometries with multi-degree-of-freedom elastic foundation contacts. FE models used fibril-reinforced poroviscoelastic swelling material models for cartilages and menisci. ResultsVolumes of knee bones, cartilages, and menisci did not significantly differ (p>0.05) across MRI sequences. MSK models estimated secondary knee kinematics during passive knee flexion tests consistent with in vivo and simulation-based values from the literature. Between the template-based and auto-meshing FE models, estimated cartilage mechanics often differed significantly (p<0.05), though differences were <15% (considering peaks during walking), i.e., <1.5 MPa for maximum principal stress, <1 percentage point for collagen fibril strain, and <3 percentage points for maximum shear strain. ConclusionThe template-based modeling provided a more rapid and robust tool than the auto-meshing approach, while the estimated knee biomechanics were comparable. Nonetheless, the auto-meshing approach might provide more accurate estimates in subjects with distinct knee irregularities, e.g., cartilage lesions. SignificanceThe MSK-FE modeling tool provides a rapid, easy-to-use, and robust approach to investigating task- and person-specific mechanical responses of the knee cartilage and menisci, holding significant promise, e.g., in personalized rehabilitation planning.

bioengineering↗

bioRxiv2023

Clinical super-resolution computed tomography of bone microstructure: application in musculoskeletal and dental imaging

ObjectivesClinical cone-beam computed tomography (CBCT) devices are limited to imaging features of half a millimeter in size. Hence, they do not allow clinical quantification of bone microstructure, which plays an important role in osteoarthritis, osteoporosis and fracture risk. For maxillofacial imaging, changes in small mineralized structures are important for dental, periodontal and ossicular chain diagnostics as well as treatment planning. Deep learning (DL)-based super-resolution (SR) models could allow for better evaluation of these microstructural details. In this study, we demonstrate a widely applicable method for increasing the spatial resolution of clinical CT images using DL, which only requires training on a limited set of data that are easy to acquire in a laboratory setting from e.g. cadaver knees. Our models are assessed rigorously for technical image quality, ability to predict bone microstructure, as well as clinical image quality of the knee, wrist, ankle and dentomaxillofacial region. Materials and methodsKnee tissue blocks from five cadavers and six total knee replacement patients as well as 14 extracted teeth from eight patients were scanned using micro-computed tomography. The images were used as training data for the developed DL-based SR technique, inspired by previous studies on single-image SR. The technique was benchmarked with an ex vivo test set, consisting of 52 small osteochondral samples imaged with clinical and laboratory CT scanners, to quantify bone morphometric parameters. A commercially available quality assurance phantom was imaged with a clinical CT device, and the technical image quality was quantified with a modulation transfer function. To visually assess the clinical image quality, CBCT studies from wrist, knee, ankle, and maxillofacial region were enhanced with SR and contrasted to interpolated images. A dental radiologist and dental surgeon reviewed maxillofacial CBCT studies of nine patients and corresponding SR predictions. ResultsThe SR models yielded a higher Pearson correlation to bone morphological parameters on the ex vivo test set compared to the use of a conventional image processing pipeline. The phantom analysis confirmed a higher spatial resolution on the images enhanced by the SR approach. A statistically significant increase of spatial resolution was seen in the third, fourth, and fifth line pair patterns. However, the predicted grayscale values of line pair patterns exceeded those of uniform areas. Musculoskeletal CBCT images showed more details on SR predictions compared to interpolation. Averaging predictions on orthogonal planes improved visual quality on perpendicular planes but could smear the details for morphometric analysis. SR in dental imaging allowed to visualize smaller mineralized structures in the maxillofacial region, however, some artifacts were observed near the crown of the teeth. The readers assessed mediocre overall scores in all categories for both CBCT and SR. Although not statistically significant, the dental radiologist slightly preferred the original CBCT images. The dental surgeon scored one of the SR models slightly higher compared to CBCT. The interrater variability {kappa} was mostly low to fair. The source code (https://doi.org/10.5281/zenodo.8041943) and pretrained SR networks (https://doi.org/10.17632/4xvx4p9tzv.1) are publicly available. ConclusionsUtilizing experimental laboratory imaging modalities in model training could allow pushing the spatial resolution limit beyond state-of-the-art clinical musculoskeletal and dental CBCT imaging. Implications of SR include higher patient throughput, more precise diagnostics, and disease interventions at an earlier state. However, the grayscale distribution of the images is modified, and the predictions are limited to depicting the mineralized structures rather than estimating density or tissue composition. Finally, while the musculoskeletal images showed promising results, a larger maxillofacial dataset would be recommended for training SR models in dental applications.

bioinformatics↗

bioRxiv2022

Associations of human femoral condyle cartilage structure and composition with viscoelastic and constituent-specific material properties at different stages of osteoarthritis

The relationships between structure and function in human knee femoral cartilage are not well-known at different stages of osteoarthritis. Thus, we characterized the depth-dependent composition and structure of normal and osteoarthritic human femoral condyle cartilage (n = 47) and related them to their viscoelastic and constituent-specific mechanical properties. We observed that, in superficial cartilage, the collagen network disorganization and proteoglycan loss were associated with the smaller initial fibril network modulus (collagen pretension). Furthermore, the proteoglycan loss was associated with the greater strain-dependent fibril network modulus (a measure of nonlinear mechanical behavior). The proteoglycan loss was also associated with greater cartilage viscosity at a low loading frequency (0.005 Hz), while the disorganization of the collagen network was associated with greater cartilage viscosity at a high loading frequency (1 Hz). Our results suggest that proteoglycan degradation and collagen disorganization reduce the pretension of the collagen network while proteoglycan degradation also increases the nonlinear mechanical response of the collagen network. Further, the results also highlight that proteoglycan degradation and collagen disorganization increase the viscosity of cartilage, but their contribution to increased viscosity occurs in completely different loading frequencies.

biophysics↗

bioRxiv2021

A musculoskeletal finite element model of rat knee joint for evaluating cartilage biomechanics during gait

Abnormal loading of the knee due to injuries or obesity is thought to contribute to the development of osteoarthritis (OA). Small animal models have been used for studying OA progression mechanisms. However, numerical models to study cartilage responses under dynamic loading in preclinical animal models have not been developed. Here we present a musculoskeletal finite element (FE) model of a rat knee joint to evaluate cartilage biomechanical responses during a gait cycle. The rat knee joint geometries were obtained from a 3-D MRI dataset and the boundary conditions regarding loading in the joint were extracted from a musculoskeletal model of the rat hindlimb. The fibril-reinforced poroelastic (FRPE) properties of the rat cartilage were derived from data of mechanical indentation tests. Our numerical results showed the relevance of simulating anatomical and locomotion characteristics in the rat knee joint for estimating tissue responses such as contact pressures, stresses, strains, and fluid pressures. We found that the contact pressure and maximum principal strain were virtually constant in the medial compartment whereas they showed the highest values at the beginning of the gait cycle in the lateral compartment. Furthermore, we found that the maximum principal stress increased during the stance phase of gait, with the greatest values at midstance. We anticipate that our approach serves as a first step towards investigating the effects of gait abnormalities on the adaptation and degeneration of rat knee joint tissues and could be used to evaluate biomechanically-driven mechanisms of the progression of OA as a consequence of joint injury or obesity. Author SummaryOsteoarthritis is a disease of the musculoskeletal system which is characterized by the degradation of articular cartilage. Changes in the knee loading after injuries or obesity contribute to the development of cartilage degeneration. Since injured cartilage cannot be reversed back to intact conditions, small animal models have been widely used for investigating osteoarthritis progression mechanisms. Moreover, experimental studies have been complemented with numerical models to overcome inherent limitations such as cost, difficulties to obtain accurate measures and replicate degenerative situations in the knee joint. However, computational models to study articular cartilage responses under dynamic loading in small animal models have not been developed. Thus, here we present a musculoskeletal finite element model of a rat knee joint to evaluate cartilage biomechanical responses during gait. Our computational model considers both the anatomical and locomotion characteristics of the rat knee joint for estimating mechanical responses in the articular cartilage. We suggest that our approach can be used to investigate tissue adaptations based on the mechanobiological responses of the cartilage to prevent the progression of osteoarthritis.

bioengineering↗

bioRxiv2021

Cyclic loading regime considered beneficial does not protect injured and interleukin-1-inflamed cartilage from post-traumatic osteoarthritis

Post-traumatic osteoarthritis is a degenerative musculoskeletal condition where homeostasis of articular cartilage is perturbated by lesions and inflammation, leading to abnormal tissue-level loading. These mechanisms have rarely been included simultaneously in in vitro osteoarthritis models. We modeled the early disease progression in bovine cartilage regulated by the coaction of (1) mechanical injury, (2) pro-inflammatory interleukin-1 challenge, and (3) cyclic loading mimicking walking and considered beneficial (15% strain, 1 Hz). Surprisingly, cyclic loading did not protect cartilage from accelerated glycosaminoglycan loss over 12 days of interleukin-1-culture despite promoting aggrecan biosynthesis. Our time-dependent data suggest that this loading regime could be beneficial in the first days following injury but later turn detrimental in interleukin-1-inflamed cartilage. Consequently, early anti-catabolic drug intervention may inhibit, whereas cyclic loading during chronic inflammation may promote osteoarthritis progression. Our data on the early stages of post-traumatic osteoarthritis could be utilized in the development of countermeasures for disease progression.

pathology↗

bioRxiv2021

An EMG-assisted Muscle-Force Driven Finite Element Analysis Pipeline to Investigate Joint- and Tissue-Level Mechanical Responses in Functional Activities: Towards a Rapid Assessment Toolbox

Joint tissue mechanics (e.g., stress and strain) are believed to have a major involvement in the onset and progression of musculoskeletal disorders, e.g., knee osteoarthritis (KOA). Accordingly, considerable efforts have been made to develop musculoskeletal finite element (MS-FE) models to estimate highly-detailed tissue mechanics that predict cartilage degeneration. However, creating such models is time-consuming and requires advanced expertise. This limits these complex, yet promising MS-FE models to research applications with few participants and making the models impractical for clinical assessments. Also, these previously developed MS-FE models are not assessed for any activities other than the gait. This study introduces and validates a semi-automated rapid state-of-the-art MS-FE modeling and simulation toolbox incorporating an electromyography (EMG) assisted MS model and a muscle-force driven FE model of the knee with fibril-reinforced poro(visco)elastic cartilages and menisci. To showcase the usability of the pipeline, we estimated joint- and tissue-level knee mechanics in 15 KOA individuals performing different daily activities. The pipeline was validated by comparing the estimated muscle activations and joint mechanics to existing experimental data. Also, to examine the importance of EMG-assisted MS analyses, results were compared against outputs from the same FE models but driven by static-optimization-based MS models. The EMG-assisted MS-FE pipeline bore a closer resemblance to experiments, compared to the static-optimization-based MS-FE pipeline. More importantly, the developed pipeline showed great potentials as a rapid MS-FE analysis toolbox to investigate multiscale knee mechanics during different activities of individuals with KOA.

biophysics↗

bioRxiv2020

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

PurposeOnly little is known how calcified cartilage (CC) structure changes during exercise, aging and disease. CC thickness (CC.Th) can be analyzed using conventional histological sections. Micro-computed tomography (CT) allows for three-dimensional (3D) imaging of mineralized tissues, however, the segmentation between bone and CC is challenging. Here, we present state-of-the-art deep learning segmentation for CT images to enable assessment of CC morphology. MethodsSixteen knees from twelve New Zealand White rabbits were dissected into osteochondral samples from six anatomical regions: lateral and medial femoral condyles, lateral and medial tibial plateaus, femoral groove and patella (n = 96). Samples were imaged with CT and processed for conventional histology. Manually segmented CC from the histology and reconstructed CT images was used as the gold standard to train segmentation models with different encoder-decoder architectures. The models with the greatest out-of-fold evaluation Dice score were used for automated CC.Th analysis. Subsequently, the automated CC.Th analysis was compared across a total of 24 regions, co-registered between the imaging modalities, using Pearson correlation and Bland-Altman analyses. Finally, the anatomical variation in CC.Th was assessed via a Linear Mixed Model analysis. ResultsThe best segmentation models yielded average Dice scores of 0.891 and 0.807 for histology and CT segmentation, respectively. The correlation between the co-registered regions across the modalities was strong (r = 0.897). The Bland-Altman analysis yielded a bias of 21.9 m and a standard deviation of 21.5 m between the methods. Finally, both methods could separate the CC morphology between the patella, femoral, and tibial regions (p < 0.001). ConclusionThe presented method allows for ex vivo 3D assessment of CC.Th in an automated and non-destructive manner. We demonstrated its utility by quantifying CC.Th in different anatomical regions. CC.Th was the thickest in the patella and the thinnest in the tibial plateau. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/260992v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@19b351aorg.highwire.dtl.DTLVardef@bb6efforg.highwire.dtl.DTLVardef@1744ba0org.highwire.dtl.DTLVardef@1113de_HPS_FORMAT_FIGEXP M_FIG C_FIG We present a CT-based method with deep learning segmentation for analyzing calcified cartilage thickness (CC.Th). The method is compared throughout the study against conventional histology. The comparison against co-registered regions yielded a strong Pearson correlation (r = 0.90). Both methods were able to separate the CC.Th properties between tibia, femur, and patella.

bioinformatics↗