This paper introduces a new approach to automatically quantify the severity of knee OA using X-ray images. Automatically quantifying knee OA severity involves two steps: first, automatically localizing the knee joints; next, classifying the localized knee joint images. We introduce a new approach to automatically detect the knee joints using a fully convolutional neural network (FCN). We train convolutional neural networks (CNN) from scratch to automatically quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross-entropy and mean-squared loss. This joint training further improves the overall quantification of knee OA severity, with the added benefit of naturally producing simultaneous multi-class classification and regression outputs. Two public datasets are used to evaluate our approach, the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST), with extremely promising results that outperform existing approaches.
Previously, Shamir et. al. @cite_11 proposed template matching to automatically detect and extract the knee joints. This method is slow for large datasets such as OAI, and the accuracy and precision of detecting knee joints is low. In our previous study, we introduced an SVM-based method for automatically detecting the center of knee joints @cite_1 and extract a fixed region with reference to the detected center as the ROI. This method is also not highly accurate and there is a compromise in the aspect ratio of the extracted knee joints that affects the overall quantification.
@cite_1 @cite_11
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This paper introduces a new approach to automatically quantify the severity of knee OA using X-ray images. Automatically quantifying knee OA severity involves two steps: first, automatically localizing the knee joints; next, classifying the localized knee joint images. We introduce a new approach to automatically detect the knee joints using a fully convolutional neural network (FCN). We train convolutional neural networks (CNN) from scratch to automatically quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross-entropy and mean-squared loss. This joint training further improves the overall quantification of knee OA severity, with the added benefit of naturally producing simultaneous multi-class classification and regression outputs. Two public datasets are used to evaluate our approach, the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST), with extremely promising results that outperform existing approaches. Current noninvasive or minimally invasive methods for evaluating in vivo knee kinematics are inadequate for accurate determination of dynamic joint function due to limited accuracy and or insufficient sampling rates. A three-dimensional (3-D) model-based method is presented to estimate skelet al motion of the knee from high-speed sequences of biplane radiographs. The method implicitly assumes that geometrical features cannot be detected reliably and an exact segmentation of bone edges is not always feasible. An existing biplane radiograph system was simulated as two separate single-plane radiograph systems. Position and orientation of the underlying bone was determined for each single-plane view by generating projections through a 3-D volumetric model (from computed tomography), and producing an image (digitally reconstructed radiograph) similar (based on texture information and rough edges of bone) to the two-dimensional radiographs. The absolute 3-D pose was determined using known imaging geometry of the biplane radiograph system and a 3-D line intersection method. Results were compared to data of known accuracy, obtained from a previously established bone-implanted marker method. Difference of controlled in vitro tests was on the order of 0.5 mm for translation and 1.4 spl deg for rotation. A biplane radiograph sequence of a canine hindlimb during treadmill walking was used for in vivo testing, with differences on the order of 0.8 mm for translation and 2.5 spl deg for rotation. Osteoarthritic (OA) changes in knee joints can be assessed by analyzing the structure of trabecular bone (TB) in the tibia. This analysis is performed on TB regions selected manually by a human operator on x-ray images. Manual selection is time-consuming, tedious, and expensive. Even if a radiologist expert or highly trained person is available to select regions, high inter- and intraobserver variabilities are still possible. A fully automated image segmentation method was, therefore, developed to select the bone regions for numerical analyses of changes in bone structures. The newly developed method consists of image preprocessing, delineation of cortical bone plates (active shape model), and location of regions of interest (ROI). The method was trained on an independent set of 40 x-ray images. Automatically selected regions were compared to the ''gold standard'' that contains ROIs selected manually by a radiologist expert on 132 x-ray images. All images were acquired from subjects locked in a standardized standing position using a radiography rig. The size of each ROI is 12.8x12.8 mm. The automated method results showed a good agreement with the gold standard [similarity index (SI)=0.83 (medial) and 0.81 (lateral) and the offset=[-1.78, 1.27]x[-0.65,0.26] mm (medial) and [-2.15, 1.59]x[-0.58, 0.52] mm (lateral)]. Blandmore » and Altman plots were constructed for fractal signatures, and changes of fractal dimensions (FD) to region offsets calculated between the gold standard and automatically selected regions were calculated. The plots showed a random scatter and the 95 confidence intervals were (-0.006, 0.008) and (-0.001, 0.011). The changes of FDs to region offsets were less than 0.035. Previous studies showed that differences in FDs between non-OA and OA bone regions were greater than 0.05. ROIs were also selected by a second radiologist and then evaluated. Results indicated that the newly developed method could replace a human operator and produces bone regions with an accuracy that is sufficient for fractal analyses of bone texture.« less Summary Objective To determine whether computer-based analysis can detect features predictive of osteoarthritis (OA) development in radiographically normal knees. Method A systematic computer-aided image analysis method weighted neighbor distances using a compound hierarchy of algorithms representing morphology (WND-CHARM) was used to analyze pairs of weight-bearing knee X-rays. Initial X-rays were all scored as normal Kellgren–Lawrence (KL) grade 0, and on follow-up approximately 20 years later either developed OA (defined as KL grade=2) or remained normal. Results The computer-aided method predicted whether a knee would change from KL grade 0 to grade 3 with 72 accuracy ( P P Conclusion Radiographic features detectable using a computer-aided image analysis method can predict the future development of radiographic knee OA.