A Lightweight CNN and Joint Shape-Joint Space (JS2) Descriptor for Radiological Osteoarthritis Detection

TL;DR

This paper introduces a novel automated method combining joint shape analysis and CNN-based bone texture features to improve radiographic osteoarthritis detection, achieving state-of-the-art accuracy.

Contribution

It is the first to describe bone texture using CNN and to fuse shape and texture features for OA detection in knee radiographs.

Findings

Achieved AUC of 95.21% in OA detection.

Fused shape and texture features outperform individual features.

Validated on large multi-center datasets.

Abstract

Knee osteoarthritis (OA) is very common progressive and degenerative musculoskeletal disease worldwide creates a heavy burden on patients with reduced quality of life and also on society due to financial impact. Therefore, any attempt to reduce the burden of the disease could help both patients and society. In this study, we propose a fully automated novel method, based on combination of joint shape and convolutional neural network (CNN) based bone texture features, to distinguish between the knee radiographs with and without radiographic osteoarthritis. Moreover, we report the first attempt at describing the bone texture using CNN. Knee radiographs from Osteoarthritis Initiative (OAI) and Multicenter Osteoarthritis (MOST) studies were used in the experiments. Our models were trained on 8953 knee radiographs from OAI and evaluated on 3445 knee radiographs from MOST. Our results demonstrate that fusing the proposed shape and texture parameters achieves the state-of-the art performance in radiographic OA detection yielding area under the ROC curve (AUC) of 95.21%