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

Abstract. Osteoarthritis (OA) is a joint disease affecting hundreds of millions of people worldwide. In basic research, accurate ex vivo measures are needed for assessing OA severity. The standard method for this is the histopathological grading of stained thin tissue sections. However, the methods are destructive, time-consuming, do not describe the full sample volume and provide subjective results. Contrast-enhanced micro-computed tomography (CEμCT) -based grading with phosphotungstic acid -stain was previously developed to address some of these issues. Aim of this study was to investigate the possibility of automating this process. Osteochondral tissue cores were harvested from total knee arthroplasty patients (n = 34, N = 19, Ø = 2 mm, n = 15, N = 5, Ø = 4 mm) and asymptomatic cadavers (n = 30, N = 2, Ø = 4 mm). Samples were imaged with CEμCT, reconstructed and graded manually. Subsequently, the reconstructions were loaded into an ad hoc developed Python software, where volumes-of-interest (VOI) were extracted from different cartilage zones: surface zone (SZ), deep zone (DZ) and calcified zone (CZ) and collapsed into two-dimensional texture images. Normalized images underwent Median Robust Extended Local Binary Pattern (MRELBP) -algorithm to extract the features, with subsequent dimensionality reduction. Ridge and logistic regression models were trained with L2 regularization against the ground truth for the small samples (Ø = 2 mm) using leave-one-patient-out cross-validation. Trained models were then evaluated on the large samples (Ø = 4 mm). Performance of the models were assessed using Spearman’s correlation, Area under the Receiver Operating Characteristic Curve (AUC) and Average Precision (AP). Highest performance on both models was for the SZ. Strong correlation was observed on ridge regression (ρ = 0.68, p < 0.0001), as well as high AUC and AP values for the logistic regression (AUC = 0.92, AP = 0.89) for the small samples. Using the large samples, similar findings were observed with slightly reduced values (ρ = 0.55, p = 0.0001, AUC = 0.86, AP = 0.89). Moderate results were observed for CZ and DZ models (ρ = 0.54 and 0.38, AUC = 0.77 and 0.72, AP = 0.71 and 0.50, respectively). Evaluation on the large samples resulted in performance decrease on CZ models (ρ = 0.29, AUC = 0.63, AP = 0.62), while surprisingly performance increased on DZ logistic regression model (ρ = 0.34, AUC = 0.72, AP = 0.83). Obtained results indicate that automating the 3D CEμCT histopathological grading is feasible. However, with low number of samples, models are better suited for binary detection of sample degenerative features, rather than predicting a detailed grade. To facilitate model generalization on new data, similar data acquisition protocol should be used on all samples. The proposed methods have potential to aid OA researchers and pathologists in 3D histopathological grading, introducing more objectivity to the grading process. This thesis presents the conducted study in detail, and provides an extensive review related to the osteochondral unit, CEμCT imaging, as well as statistical learning machines

Direct reduction of chromite using the FFC Cambridge method

Abstract. The guest for finding fossil-free alternatives to today’s metal production processes goes strong. The FFC Cambridge process is one alternative to provide CO₂-lean or CO₂-free alternatives to conventional metal production processes. The FFC Cambridge process has been used to reduce a plethora of metal oxides via electrolysis, but the reduction of sintered chromite pellets from the industrial steel belt sintering process has not yet been experimented with. The main goal of this thesis was to study the applicability of the FFC Cambridge direct reduction method for reducing the sintered chromite pellets from Outokumpu Tornio works to ferrochrome. Also, the possibility of using inert anodes was reviewed together with discussion about the issues faced during the experiments that would pose challenges to the scale up of the process. The experimental part of the study presents the experimental apparatus and the factors that needed to be evaluated before the experiments such as safety and experimental constraints. Additionally, the composition of chromite was analysed with FESEM-EDS and XRF. An experiment was conducted under voltage of 2.8 V, temperature of 900 °C and one hour electrolysis time in a CaCl₂ electrolyte. The results showed promise in the reduction of some chromite particles, but many challenges were identified for the future experiments and the possible scale-up of the process such as the low mechanical integrity of the pellets, low efficiency of the process, the expensiveness of the process, the need to reoptimize the ferrochromium manufacturing process, and the corrosive nature of the electrolyte.Kromiitin suorapelkistys FFC Cambridge -menetelmällä. Tiivistelmä. Fossiilivapaiden metallintuotantoprosessien etsintä käy kuumana. FFC Cambridge -menetelmä on yksi vaihtoehto tällaiseksi CO₂-päästöjä pienentäväksi tai jopa kokonaan poistavaksi tuotantoprosessiksi. FFC Cambridge -menetelmää on käytetty jo useiden eri metallien pelkistykseen, mutta sitä ei ole vielä kokeiltu teräsnauhasintrattujen kromiittipellettien pelkistykseen. Tämän diplomityön tavoitteena oli tutkia FFC Cambridge -menetelmän soveltuvuutta Outokummun sintrattujen kromiittipellettien pelkistämiseen. Lisäksi työssä selvitettiin erilaisten inerttien elektrodien käyttömahdollisuuksia sekä pohdittiin, mitä haasteita FFC Cambridge -menetelmälle on mahdolliseen tuotannon ylösskaalaukseen laboratoriokokeiden perusteella. Työn kokeellisessa osassa esiteltiin käytetty koelaitteisto ja tutkittiin, mitä turvallisuusseikkoja kokeissa tulee ottaa huomioon. Lisäksi koesuunnitelmassa pohdittiin koeolosuhteiden mahdollisia rajoja ja selvitettiin kromiitin todellista koostumusta FESEM-EDS:n sekä XRF:n avulla. Työssä tehtiin koe 2,8 V jännitteellä, 900 °C lämpötilassa koeajan ollessa yksi tunti. Elektrolyyttinä toimi CaCl₂. Kokeen tulokset olivat toivoa herättäviä, sillä pieni määrä ferrokromia saatiin pelkistettyä onnistuneesti. Kokeet kuitenkin toivat esille useita eri haasteita seuraaville mahdollisille kokeille sekä mahdolliselle teolliseen mittakaavaan skaalaukselle: pellettien huono mekaaninen kestävyys, prosessin alhainen tehokkuus, hintavuus, tarve suunnitella ferrokromin valmistusprosessi uudelleen tätä prosessointimenetelmää varten ja elektrolyytin korroosiovaikutus

Atypical perceptual narrowing in prematurely born infants is associated with compromised language acquisition at 2 years of age

Background: Early auditory experiences are a prerequisite for speech and language acquisition. In healthy children, phoneme discrimination abilities improve for native and degrade for unfamiliar, socially irrelevant phoneme contrasts between 6 and 12 months of age as the brain tunes itself to, and specializes in the native spoken language. This process is known as perceptual narrowing, and has been found to predict normal native language acquisition. Prematurely born infants are known to be at an elevated risk for later language problems, but it remains unclear whether these problems relate to early perceptual narrowing. To address this question, we investigated early neurophysiological phoneme discrimination abilities and later language skills in prematurely born infants and in healthy, full-term infants. Results: Our follow-up study shows for the first time that perceptual narrowing for non-native phoneme contrasts found in the healthy controls at 12 months was not observed in very prematurely born infants. An electric mismatch response of the brain indicated that whereas full-term infants gradually lost their ability to discriminate non-native phonemes from 6 to 12 months of age, prematurely born infants kept on this ability. Language performance tested at the age of 2 years showed a significant delay in the prematurely born group. Moreover, those infants who did not become specialized in native phonemes at the age of one year, performed worse in the communicative language test (MacArthur Communicative Development Inventories) at the age of two years. Thus, decline in sensitivity to non-native phonemes served as a predictor for further language development. Conclusion: Our data suggest that detrimental effects of prematurity on language skills are based on the low degree of specialization to native language early in development. Moreover, delayed or atypical perceptual narrowing was associated with slower language acquisition. The results hence suggest that language problems related to prematurity may partially originate already from this early tuning stage of language acquisition

Contrast enhanced micro-computed tomography of cartilage and chondrocytes

Contrast enhanced micro-computed tomography (CEμCT) is a widely used, cost-efficient method for imaging soft tissues. For articular cartilage imaging, different stains are used to assess amount of cartilage constituents. Osteoarthritis progression can be monitored using CEμCT in clinical environment and laboratory. In clinical modalities, ioxaglate (Hexabrix™) is used to indirectly assess proteoglycan content of cartilage. It is a iodine based anionic stain that has inversely proportional distribution to proteoglycan content. Collagen specific contrast agents are not in clinical use yet. In vitro studies that evaluate osteoarthritis progression use histological staining as a gold standard method. Downside for this is long required time and destruction of sample for a thin, two-dimensional view of the sample. Alternatively, μCT could be used as a non-invasive tool for this when using a collagen stain. One such contrast agent is phosphotungstic acid. It has been shown to be able for three-dimensional evaluation of osteoarthritis grade in same manner as with histological stains. Cationic contrast agent (CA4+) is used in vitro to assess proteoglycan content. It binds to glycosaminoglycan side chains while also slightly accumulating around chondrons. Its advantage is that it binds to target molecule giving accurate information about proteoglycan content when compared to other contrast agents. Chondrocyte imaging with contrast agents is quite demanding. There is not a commonly accepted stain for chondrocytes up to date, but such dyes would be of high interest. Some potential stains might be gallocyanin chrome-alum or glucose-coated gold nanoparticles. A possible method for using gold nanoparticles is proposed in this thesis. There are also studies of chondron morphology using hexamethyldisilazane drying. Downside for this method is tissue shrinkage that might alter at least superficial chondrons. Competing modalities for CEμCT include magnetic resonance imaging (MRI), Fourier transform infrared spectroscopy, near-infrared spectroscopy and Raman spectroscopy. In clinical environment MRI has clear advantage as it does not require utilization of ionizing radiation to produce great soft tissue contrast. High cost and low availability however are main limitations. Spectroscopic methods do not require contrast agents, but especially infrared spectroscopy requires extensive sample preparation and these methods provide only 2D maps of the sample. Latest methods for desktop CEμCT cartilage imaging are reviewed in this thesis. Extracellular matrix assessing methods are studied in clinic and in vitro. Some chondrocyte imaging possibilities are discussed. CEμCT can be used to cost-effectively assess articular cartilage characteristics and OA progression in 3D

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

Pretrained models for super-resolution on musculoskeletal and dental computed tomography data. The three models (Baseline model, Structure model and Visual model) are trained on knee tissue blocks and extracted teeth to increase the image quality on reconstructions with 200µm voxel size.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

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

Objective: To 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 (CEμCT). Design: A total of 79 osteochondral cores from 24 total knee arthroplasty patients and two asymptomatic donors were imaged using CEμCT with phosphotungstic acid -staining. Volumes-of-interest (VOI) in surface (SZ), deep (DZ) and calcified (CZ) zones were extracted depth-wise and subjected to dimensionally reduced Local Binary Pattern -textural feature analysis. Regularized linear and logistic regression (LR) models were trained zone-wise against the manually assessed semi-quantitative histopathological CEμCT grades (diameter = 2 mm samples). Models were validated using nested leave-one-out cross-validation and an independent test set (4 mm samples). The performance was primarily assessed using Mean Squared Error (MSE) and Average Precision (AP, confidence intervals are given in square brackets). Results: Highest performance on cross-validation was observed for SZ, both on linear regression (MSE = 0.49, 0.69 and 0.71 for SZ, DZ and CZ, respectively) and LR (AP = 0.9 [0.77–0.99], 0.46 [0.28–0.67] and 0.65 [0.41–0.85] for SZ, DZ and CZ, respectively). The test set evaluations yielded increased MSE on all zones. For LR, the performance was also best for the SZ (AP = 0.85 [0.73–0.93], 0.82 [0.70–0.92] and 0.8 [0.67–0.9], for SZ, DZ and CZ, respectively). Conclusion: We present the first ML-based automatic 3D histopathological osteoarthritis (OA) grading method which also adequately perform on grading unseen data, especially in SZ. After further development, the method could potentially be applied by OA researchers since the grading software and all source codes are publicly available.Peer reviewe

Quantifying Subresolution 3D Morphology of Bone with Clinical Computed Tomography

| openaire: EC/H2020/336267/EU//3D-OA-HISTOThe aim of this study was to quantify sub-resolution trabecular bone morphometrics, which are also related to osteoarthritis (OA), from clinical resolution cone beam computed tomography (CBCT). Samples (n = 53) were harvested from human tibiae (N = 4) and femora (N = 7). Grey-level co-occurrence matrix (GLCM) texture and histogram-based parameters were calculated from CBCT imaged trabecular bone data, and compared with the morphometric parameters quantified from micro-computed tomography. As a reference for OA severity, histological sections were subjected to OARSI histopathological grading. GLCM and histogram parameters were correlated to bone morphometrics and OARSI individually. Furthermore, a statistical model of combined GLCM/histogram parameters was generated to estimate the bone morphometrics. Several individual histogram and GLCM parameters had strong associations with various bone morphometrics (|r| > 0.7). The most prominent correlation was observed between the histogram mean and bone volume fraction (r = 0.907). The statistical model combining GLCM and histogram-parameters resulted in even better association with bone volume fraction determined from CBCT data (adjusted R-2 change = 0.047). Histopathology showed mainly moderate associations with bone morphometrics (|r| > 0.4). In conclusion, we demonstrated that GLCM- and histogram-based parameters from CBCT imaged trabecular bone (ex vivo) are associated with sub-resolution morphometrics. Our results suggest that sub-resolution morphometrics can be estimated from clinical CBCT images, associations becoming even stronger when combining histogram and GLCM-based parameters.Peer reviewe

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

Summary Objective: To 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 (CEμCT). Design: A total of 79 osteochondral cores from 24 total knee arthroplasty patients and two asymptomatic donors were imaged using CEμCT with phosphotungstic acid -staining. Volumes-of-interest (VOI) in surface (SZ), deep (DZ) and calcified (CZ) zones were extracted depth-wise and subjected to dimensionally reduced Local Binary Pattern -textural feature analysis. Regularized linear and logistic regression (LR) models were trained zone-wise against the manually assessed semi-quantitative histopathological CEμCT grades (diameter = 2 mm samples). Models were validated using nested leave-one-out cross-validation and an independent test set (4 mm samples). The performance was primarily assessed using Mean Squared Error (MSE) and Average Precision (AP, confidence intervals are given in square brackets). Results: Highest performance on cross-validation was observed for SZ, both on linear regression (MSE = 0.49, 0.69 and 0.71 for SZ, DZ and CZ, respectively) and LR (AP = 0.9 [0.77–0.99], 0.46 [0.28–0.67] and 0.65 [0.41–0.85] for SZ, DZ and CZ, respectively). The test set evaluations yielded increased MSE on all zones. For LR, the performance was also best for the SZ (AP = 0.85 [0.73–0.93], 0.82 [0.70–0.92] and 0.8 [0.67–0.9], for SZ, DZ and CZ, respectively). Conclusion: We present the first ML-based automatic 3D histopathological osteoarthritis (OA) grading method which also adequately perform on grading unseen data, especially in SZ. After further development, the method could potentially be applied by OA researchers since the grading software and all source codes are publicly available

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

Abstract 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 &lt; 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