Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss. (July 2019)

• Record Type: Journal Article
• Authors: Chen, Pingjun; Gao, Linlin; Shi, Xiaoshuang; Allen, Kyle; Yang, Lin
• Is Part Of: Computerized medical imaging and graphics. Volume 75(2019)
• Page Start: 84
• Page End: 92
• Publication Date: 2019-07
• Subjects: Knee osteoarthritis -- Kellgren and Lawrence grading -- Ordinal loss -- Convolutional neural network; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2019.06.002
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: A customized YOLOv2 detection model is proposed for knee joint detection, considering the less varied size of knee joints. A new adjustable ordinal loss function is developed for knee joint Kellgren-Lawrence (KL) grade classification, and a general way to choose ordinal matrix is proposed. Fine-tuning multiple popular CNN models using the proposed ordinal loss, and VGG-19 model achieves the best performance on knee KL grading. Both knee joint detection and KL grading achieve state-of-the-art performance with the proposed method. Abstract: Knee osteoarthritis (OA) is one major cause of activity limitation and physical disability in older adults. Early detection and intervention can help slow down the OA degeneration. Physicians' grading based on visual inspection is subjective, varied across interpreters, and highly relied on their experience. In this paper, we successively apply two deep convolutional neural networks (CNN) to automatically measure the knee OA severity, as assessed by the Kellgren-Lawrence (KL) grading system. Firstly, considering the size of knee joints distributed in X-ray images with small variability, we detect knee joints using a customized one-stage YOLOv2 network. Secondly, we fine-tune the most popular CNN models, including variants of ResNet, VGG, and DenseNet as well as InceptionV3, to classify the detected knee joint images with a novel adjustable ordinal loss. To be specific, motivated by the ordinal nature of the knee KL grading task, we assign higher penalty to misclassification with larger distance between the predicted KL grade and the real KL grade. The baseline X-ray images from the Osteoarthritis Initiative (OAI) dataset are used for evaluation. On the knee joint detection, we achieve mean Jaccard index of 0.858 and recall of 92.2% under the Jaccard index threshold of 0.75. On the knee KL grading task, the fine-tuned VGG-19 model with the proposed ordinal loss obtains the best classification accuracy of 69.7% and mean absolute error (MAE) of 0.344. Both knee joint detection and knee KL grading achieve state-of-the-art performance. The code, dataset, and models are released athttps://github.com/PingjunChen/KneeAnalysis .