3D ultrasound biomicroscopy for assessment of cartilage repair tissue: volumetric characterisation and correlation to established classification systems

Schöne, Martin; Männicke, Nils; Somerson, Jeremy S.; Marquaß, Bastian; Henkelmann, Ralf; Mochida, Joji; Aigner, Thomas; Raum, Kay; Schulz, R.

doi:10.22203/ecm.v031a09

Cited by 9 publications

(6 citation statements)

References 22 publications

(46 reference statements)

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“…Multiple 3D histopathological grading methods for different tissues have been proposed in the literature, based on magnetic resonance imaging (MRI) 12e15 , optical imaging 16 , ultrasound 17 , and atomic force microscopy 18 . 3D grading methods could possibly serve as a reference for clinical 3D modalities, as well as higher resolution 3D techniques.…”

Section: Introductionmentioning

confidence: 99%

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

Rytky

Tiulpin

Frondelius

et al. 2020

Osteoarthritis and Cartilage

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Objective: To develop and validate a machine learning (ML) approach for automatic three-dimensional (3D) histopathological grading of osteochondral samples imaged with contrast-enhanced microcomputed tomography (CEmCT). Design: A total of 79 osteochondral cores from 24 total knee arthroplasty patients and two asymptomatic donors were imaged using CEmCT 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 CEmCT grades (diameter ¼ 2 mm samples). Models were validated using nested leave-one-out crossvalidation 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 ¼ 0.49, 0.69 and 0.71 for SZ, DZ and CZ, respectively) and LR (AP ¼ 0.9 [0.77e0.99], 0.46 [0.28e0.67] and 0.65 [0.41e0.85] 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 ¼ 0.85 [0.73e0.93], 0.82 [0.70e0.92] and 0.8 [0.67e0.9], 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.

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Section: Introductionmentioning

confidence: 99%

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

Rytky

Tiulpin

Frondelius

et al. 2020

Osteoarthritis and Cartilage

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“…Multiple 3D histopathological grading methods for different tissues have been proposed in the literature, based on magnetic resonance imaging (MRI) [12][13][14][15] , optical imaging 16 , ultrasound 17 , and atomic force microscopy 18 . 3D grading methods could possibly serve as a reference for clinical 3D modalities, as well as higher resolution 3D techniques.…”

Section: Introductionmentioning

confidence: 99%

Automating Three-dimensional Osteoarthritis Histopathological Grading of Human Osteochondral Tissue using Machine Learning on Contrast-Enhanced Micro-Computed Tomography

Rytky

Tiulpin

Frondelius

et al. 2019

Preprint

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AbstractObjectiveTo 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).DesignOsteochondral cores from 24 total knee arthroplasty patients and 2 asymptomatic cadavers (n = 34, Ø = 2 mm; n = 45, Ø = 4 mm) were imaged using CEμCT 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 CEμCT grades (Ø = 2 mm samples). Models were validated using nested leave-one-out cross-validation and an independent test set (Ø = 4 mm samples). The performance was assessed using Spearman’s correlation, Average Precision (AP) and Area under the Receiver Operating Characteristic Curve (AUC).ResultsHighest performance on cross-validation was observed for SZ, both on Ridge regression (ρ = 0.68, p < 0.0001) and LR (AP = 0.89, AUC = 0.92). The test set evaluations yielded decreased Spearman’s 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).ConclusionWe showed that the ML-based automatic 3D histopathological grading of osteochondral samples is feasible from CEμCT. The developed method can be directly applied by OA researchers since the grading software and all source codes are publicly available.

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“…Currently, 3D micro-level imaging technologies, based on optics 9 , sound 10 , 11 , ionizing radiation 12 and magnetic resonance 13 , can visualize relatively large tissue volumes (millimeters to centimeters). Such technologies could provide significant benefits over conventional section-based histopathology, such as: (1) minimal destructiveness, (2) no sectioning artefacts and (3) assessment of volumes (3D tissue structure, and composition).…”

Section: Introductionmentioning

confidence: 99%

“…Future candidates for 3D micro-level imaging, which permit histopathological evaluation of OC structure, include optical coherence tomography (OCT) 9 , photo-acoustic imaging 11 , ultrasound biomicroscopy (USBM) 10 , scanning electron microscopy (SEM) 17 , transmission electron microscopy (TEM) 18 , and micro-magnetic resonance imaging (μMRI) 13 . However, currently their resolution and contrast mechanism are either insufficient to visualize structural or compositional details reliably in a large volume (few mm 3 ) with sub-cellular resolution (1–10 μm) or they essentially miss one spatial dimension (SEM, TEM).…”

Section: Introductionmentioning

confidence: 99%

3D histopathological grading of osteochondral tissue using contrast-enhanced micro-computed tomography

Nieminen

Gahunia²,

Pritzker

et al. 2017

Osteoarthritis and Cartilage

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SummaryObjectiveHistopathological grading of osteochondral (OC) tissue is widely used in osteoarthritis (OA) research, and it is relatively common in post-surgery in vitro diagnostics. However, relying on thin tissue section, this approach includes a number of limitations, such as: (1) destructiveness, (2) sample processing artefacts, (3) 2D section does not represent spatial 3D structure and composition of the tissue, and (4) the final outcome is subjective. To overcome these limitations, we recently developed a contrast-enhanced μCT (CEμCT) imaging technique to visualize the collagenous extracellular matrix (ECM) of articular cartilage (AC). In the present study, we demonstrate that histopathological scoring of OC tissue from CEμCT is feasible. Moreover, we establish a new, semi-quantitative OA μCT grading system for OC tissue.ResultsPathological features were clearly visualized in AC and subchondral bone (SB) with μCT and verified with histology, as demonstrated with image atlases. Comparison of histopathological grades (OARSI or severity (0–3)) across the characterization approaches, CEμCT and histology, excellent (0.92, 95% CI = [0.84, 0.96], n = 30) or fair (0.50, 95% CI = [0.16, 0.74], n = 27) intra-class correlations (ICC), respectively. A new μCT grading system was successfully established which achieved an excellent cross-method (μCT vs histology) reader-to-reader intra-class correlation (0.78, 95% CI = [0.58, 0.89], n = 27).ConclusionsWe demonstrated that histopathological information relevant to OA can reliably be obtained from CEμCT images. This new grading system could be used as a reference for 3D imaging and analysis techniques intended for volumetric evaluation of OA pathology in research and clinical applications.

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3D ultrasound biomicroscopy for assessment of cartilage repair tissue: volumetric characterisation and correlation to established classification systems

Cited by 9 publications

References 22 publications

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

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

Automating Three-dimensional Osteoarthritis Histopathological Grading of Human Osteochondral Tissue using Machine Learning on Contrast-Enhanced Micro-Computed Tomography

3D histopathological grading of osteochondral tissue using contrast-enhanced micro-computed tomography

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