Knee Osteoarthritis Classification Using 3D CNN and MRI
Osteoarthritis (OA) is the most common form of arthritis and can often occur in the knee. While convolutional neural networks (CNNs) have been widely used to study medical images, the application of a 3-dimensional (3D) CNN in knee OA diagnosis is limited. This study utilizes a 3D CNN model to analy...
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doaj-179a4f45074f4bae9bc70249622153de2021-06-30T23:12:46ZengMDPI AGApplied Sciences2076-34172021-06-01115196519610.3390/app11115196Knee Osteoarthritis Classification Using 3D CNN and MRICarmine Guida0Ming Zhang1Juan Shan2Department of Computer Science, Seidenberg School of CSIS, Pace University, New York, NY 10038, USASchool of Computing and Data Science, Wentworth Institute of Technology, Boston, MA 02115, USADepartment of Computer Science, Seidenberg School of CSIS, Pace University, New York, NY 10038, USAOsteoarthritis (OA) is the most common form of arthritis and can often occur in the knee. While convolutional neural networks (CNNs) have been widely used to study medical images, the application of a 3-dimensional (3D) CNN in knee OA diagnosis is limited. This study utilizes a 3D CNN model to analyze sequences of knee magnetic resonance (MR) images to perform knee OA classification. An advantage of using 3D CNNs is the ability to analyze the whole sequence of 3D MR images as a single unit as opposed to a traditional 2D CNN, which examines one image at a time. Therefore, 3D features could be extracted from adjacent slices, which may not be detectable from a single 2D image. The input data for each knee were a sequence of double-echo steady-state (DESS) MR images, and each knee was labeled by the Kellgren and Lawrence (KL) grade of severity at levels 0–4. In addition to the 5-category KL grade classification, we further examined a 2-category classification that distinguishes non-OA (KL ≤ 1) from OA (KL ≥ 2) knees. Clinically, diagnosing a patient with knee OA is the ultimate goal of assigning a KL grade. On a dataset with 1100 knees, the 3D CNN model that classifies knees with and without OA achieved an accuracy of 86.5% on the validation set and 83.0% on the testing set. We further conducted a comparative study between MRI and X-ray. Compared with a CNN model using X-ray images trained from the same group of patients, the proposed 3D model with MR images achieved higher accuracy in both the 5-category classification (54.0% vs. 50.0%) and the 2-category classification (83.0% vs. 77.0%). The result indicates that MRI, with the application of a 3D CNN model, has greater potential to improve diagnosis accuracy for knee OA clinically than the currently used X-ray methods.https://www.mdpi.com/2076-3417/11/11/5196knee osteoarthritis classification3D MRIX-ray3D convolutional neural network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Carmine Guida Ming Zhang Juan Shan |
spellingShingle |
Carmine Guida Ming Zhang Juan Shan Knee Osteoarthritis Classification Using 3D CNN and MRI Applied Sciences knee osteoarthritis classification 3D MRI X-ray 3D convolutional neural network |
author_facet |
Carmine Guida Ming Zhang Juan Shan |
author_sort |
Carmine Guida |
title |
Knee Osteoarthritis Classification Using 3D CNN and MRI |
title_short |
Knee Osteoarthritis Classification Using 3D CNN and MRI |
title_full |
Knee Osteoarthritis Classification Using 3D CNN and MRI |
title_fullStr |
Knee Osteoarthritis Classification Using 3D CNN and MRI |
title_full_unstemmed |
Knee Osteoarthritis Classification Using 3D CNN and MRI |
title_sort |
knee osteoarthritis classification using 3d cnn and mri |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-06-01 |
description |
Osteoarthritis (OA) is the most common form of arthritis and can often occur in the knee. While convolutional neural networks (CNNs) have been widely used to study medical images, the application of a 3-dimensional (3D) CNN in knee OA diagnosis is limited. This study utilizes a 3D CNN model to analyze sequences of knee magnetic resonance (MR) images to perform knee OA classification. An advantage of using 3D CNNs is the ability to analyze the whole sequence of 3D MR images as a single unit as opposed to a traditional 2D CNN, which examines one image at a time. Therefore, 3D features could be extracted from adjacent slices, which may not be detectable from a single 2D image. The input data for each knee were a sequence of double-echo steady-state (DESS) MR images, and each knee was labeled by the Kellgren and Lawrence (KL) grade of severity at levels 0–4. In addition to the 5-category KL grade classification, we further examined a 2-category classification that distinguishes non-OA (KL ≤ 1) from OA (KL ≥ 2) knees. Clinically, diagnosing a patient with knee OA is the ultimate goal of assigning a KL grade. On a dataset with 1100 knees, the 3D CNN model that classifies knees with and without OA achieved an accuracy of 86.5% on the validation set and 83.0% on the testing set. We further conducted a comparative study between MRI and X-ray. Compared with a CNN model using X-ray images trained from the same group of patients, the proposed 3D model with MR images achieved higher accuracy in both the 5-category classification (54.0% vs. 50.0%) and the 2-category classification (83.0% vs. 77.0%). The result indicates that MRI, with the application of a 3D CNN model, has greater potential to improve diagnosis accuracy for knee OA clinically than the currently used X-ray methods. |
topic |
knee osteoarthritis classification 3D MRI X-ray 3D convolutional neural network |
url |
https://www.mdpi.com/2076-3417/11/11/5196 |
work_keys_str_mv |
AT carmineguida kneeosteoarthritisclassificationusing3dcnnandmri AT mingzhang kneeosteoarthritisclassificationusing3dcnnandmri AT juanshan kneeosteoarthritisclassificationusing3dcnnandmri |
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