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Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity

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Deep Learners and Deep Learner Descriptors for Medical Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 186))

Abstract

Feature learning refers to techniques that learn to transform raw data input into an effective representation for further higher-level processing in many computer vision tasks. This chapter presents the investigations and the results of feature learning using convolutional neural networks to automatically assess knee osteoarthritis (OA) severity and the associated clinical and diagnostic features of knee OA from radiographs (X-ray images). Also, this chapter demonstrates that feature learning in a supervised manner is more effective than using conventional handcrafted features for automatic detection of knee joints and fine-grained knee OA image classification. In the general machine learning approach to automatically assess knee OA severity, the first step is to localize the region of interest that is to detect and extract the knee joint regions from the radiographs, and the next step is to classify the localized knee joints based on a radiographic classification scheme such as Kellgren and Lawrence grades. First, the existing approaches for detecting (or localizing) the knee joint regions based on handcrafted features are reviewed and outlined in this chapter. Next, three new approaches are introduced: (1) to automatically detect the knee joint region using a fully convolutional network, (2) to automatically assess the radiographic knee OA using CNNs trained from scratch for classification and regression of knee joint images to predict KL grades in ordinal and continuous scales, and (3) to quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross entropy and mean-squared error using multi-objective convolutional learning. The results from these methods show progressive improvement in the overall quantification of the knee OA severity. Two public datasets: the OAI and the MOST are used to evaluate the approaches with promising results that outperform existing approaches. In summary, this work primarily contributes to the field of automated methods for localization (automatic detection) and quantification (image classification) of radiographic knee OA.

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Notes

  1. 1.

    Weighted Neighbor Distance using Compound Hierarchy of Algorithms Representing Morphology.

  2. 2.

    ImageNet Large Scale Visual Recognition Challenge.

  3. 3.

    Osteoarthritis Initiative.

  4. 4.

    Multicenter Osteoarthritis Study.

  5. 5.

    Osteoarthritis Research Society International.

  6. 6.

    Western Ontario and McMaster Universities Osteoarthritis Index.

  7. 7.

    Fisher score is one of the widely used methods for determining the most relevant features for classification.

  8. 8.

    https://statistics.laerd.com/spss-tutorials/ordinal-regression-using-spss-statistics.php.

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Acknowledgements

This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under grant numbers SFI/12/RC/2289 and 15/SIRG/3283.

The OAI is a public-private partnership comprised of five contracts (N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2-2261; N01-AR-2-2262) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Merck Research Laboratories; Novartis Pharmaceuticals Corporation, GlaxoSmithKline; and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health.

MOST is comprised of four cooperative grants (Felson—AG18820; Torner—AG18832; Lewis—AG18947; and Nevitt—AG19069) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by MOST study investigators. This manuscript was prepared using MOST data and does not necessarily reflect the opinions or views of MOST investigators.

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  1. Insight Centre for Data Analytics, Dublin City University, Dublin, Ireland

    Joseph Antony, Kevin McGuinness, Kieran Moran & Noel E. O’Connor

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  1. Joseph Antony
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  2. Kevin McGuinness
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  4. Noel E. O’Connor
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  1. Department of Information Engineering, University of Padova, Padova, Italy

    Loris Nanni

  2. Computer Information Systems, Missouri State University, Springfield, MO, USA

    Sheryl Brahnam

  3. Department of Information Technology and Cybersecurity, Missouri State University, Springfield, MO, USA

    Rick Brattin

  4. Department of Information Engineering, Intelligent Autonomous Systems Lab, Padova, Padova, Italy

    Stefano Ghidoni

  5. University of Technology, Sydney, Australia

    Lakhmi C. Jain

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Antony, J., McGuinness, K., Moran, K., O’Connor, N.E. (2020). Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity. In: Nanni, L., Brahnam, S., Brattin, R., Ghidoni, S., Jain, L. (eds) Deep Learners and Deep Learner Descriptors for Medical Applications. Intelligent Systems Reference Library, vol 186. Springer, Cham. https://doi.org/10.1007/978-3-030-42750-4_2

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