Rytky, S. J. O., Tiulpin, A., Frondelius, T., Finnilä, M. A. J., Karhula, S. S., Leino, J., Pritzker, K. P. H., Valkealahti, M., Lehenkari, P., Joukainen, A., Kröger, H., Nieminen, H. J., & Saarakkala, S. (2020). Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography. Osteoarthritis and Cartilage, 28(8), 1133–1144. https://doi.org/10.1016/j.joca.2020.05.002
Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography
|Author:||Rytky, S.J.O.1; Tiulpin, A.1,2; Frondelius, T.1;|
1Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
2Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
3Medical Research Center, University of Oulu, Oulu, Finland
4Department of Laboratory Medicine and Pathobiology, Surgery University of Toronto, Toronto, Ontario, Canada
5Mount Sinai Hospital, Toronto, Ontario, Canada
6Department of Surgery and Intensive Care, Oulu University Hospital, Oulu, Finland
7Cancer and Translational Medical Research Unit, Faculty of Medicine, University of Oulu, Oulu, Finland
8Department of Orthopaedics, Traumatology and Hand Surgery, Kuopio University Hospital, Kuopio, Finland
9Dept. of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
|Online Access:||PDF Full Text (PDF, 2.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020081460494
|Publish Date:|| 2020-08-14
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.
Osteoarthritis and cartilage
|Pages:||1133 - 1144|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
217 Medical engineering
The financial support of the Academy of Finland (grants no. 268378, and 303786); Sigrid Juselius Foundation; European Research Council under the European Union's Seventh Framework Programme (FP/2007–2013) / ERC Grant Agreement no. 336267; Kaute Foundation; and the strategic funding of University of Oulu are acknowledged.
|Academy of Finland Grant Number:||
268378 (Academy of Finland Funding decision)
303786 (Academy of Finland Funding decision)
© 2020 The Author(s). Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).