Biology subjects

Finnilä, M. A. J.

Publications and source records attributed to Finnilä, M. A. J..

5 recordsLinked to original sources

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↗

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↗

bioRxiv2021

Mineral crystal thickness in calcified cartilage and subchondral bone in healthy and osteoarthritic knees

Osteoarthritis (OA) is the most common joint disease globally. In OA, articular cartilage degradation is often accompanied with sclerosis of the subchondral bone. However, the association between OA and tissue mineralization at the nanostructural level is currently not understood. Especially, it is technically challenging to identify calcified cartilage, where relevant but poorly understood pathological processes like tidemark multiplication and advancement occur. Here, we used state-of-the-art micro-focus small-angle X-ray scattering with high 5{micro}m spatial resolution to determine mineral crystal thickness in human subchondral bone and calcified cartilage. Specimens with a wide spectrum of OA severities were acquired from the medial and lateral compartments of medial compartment knee OA patients (n=15) and cadaver knees (n=10). For the first time, we identified a well-defined layer of calcified cartilage associated with pathological tidemark multiplication, containing 0.32nm thicker crystals compared to the rest of calcified cartilage. In addition, we found 0.2nm thicker mineral crystals in both tissues of the lateral compartment in OA compared with healthy knees, indicating a loading-related disease process since the lateral compartment is typically less loaded in medial compartment knee OA. Furthermore, the crystal thickness of the subchondral bone was lower with increasing histopathological OA severity. In summary, we report novel changes in mineral crystal thickness during OA. Our data suggest that unloading in the knee is associated with the growth of mineral crystals, which is especially evident in the calcified cartilage. In the subchondral bone, mineral crystals become thinner with increasing OA severity, which indicates new bone formation with sclerosis. One Sentence SummaryMineral crystal thickness increases with osteoarthritis in the lateral condyle that is typically unloaded.

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↗

bioRxiv2019

Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography

ObjectiveTo develop and validate a machine learning (ML) approach for automatic three-dimensional (3D) histopathological grading of osteochondral samples imaged with contrast-enhanced micro-computed tomography (CECT).\n\nDesignOsteochondral cores from 24 total knee arthroplasty patients and 2 asymptomatic cadavers (n = 34, O = 2 mm; n = 45, O = 4 mm) were imaged using CECT with phosphotungstic acid-staining. Volumes-of-interest (VOI) in surface (SZ), deep (DZ) and calcified (CZ) zones were extracted depthwise and subjected to dimensionally reduced Local Binary Pattern-textural feature analysis. Regularized Ridge and Logistic regression (LR) models were trained zone-wise against the manually assessed semi-quantitative histopathological CECT grades (O = 2 mm samples). Models were validated using nested leave-one-out cross-validation and an independent test set (O = 4 mm samples). The performance was assessed using Spearmans correlation, Average Precision (AP) and Area under the Receiver Operating Characteristic Curve (AUC).\n\nResultsHighest performance on cross-validation was observed for SZ, both on Ridge regression ({rho} = 0.68, p < 0.0001) and LR (AP = 0.89, AUC = 0.92). The test set evaluations yielded decreased Spearmans correlations on all zones. For LR, performance was almost similar in SZ (AP = 0.89, AUC = 0.86), decreased in CZ (AP = 0.71[->]0.62, AUC = 0.77[->]0.63) and increased in DZ (AP = 0.50[->]0.83, AUC = 0.72[->]0.72).\n\nConclusionWe showed that the ML-based automatic 3D histopathological grading of osteochondral samples is feasible from CECT. The developed method can be directly applied by OA researchers since the grading software and all source codes are publicly available.

bioengineering↗