Osteoarthritis (OA) is a common musculoskelet al condition typically diagnosed from radiographic assessment after clinical examination. However, a visual evaluation made by a practitioner suffers from subjectivity and is highly dependent on the experience. Computer-aided diagnostics (CAD) could improve the objectivity of knee radiographic examination. The first essential step of knee OA CAD is to automatically localize the joint area. However, according to the literature this task itself remains challenging. The aim of this study was to develop novel and computationally efficient method to tackle the issue. Here, three different datasets of knee radiographs were used (n = 473 93 77) to validate the overall performance of the method. Our pipeline consists of two parts: anatomically-based joint area proposal and their evaluation using Histogram of Oriented Gradients and the pre-trained Support Vector Machine classifier scores. The obtained results for the used datasets show the mean intersection over the union equal to: 0.84, 0.79 and 0.78. Using a high-end computer, the method allows to automatically annotate conventional knee radiographs within 14-16ms and high resolution ones within 170ms. Our results demonstrate that the developed method is suitable for large-scale analyses.
In the literature, multiple approaches have been used to localize ROIs within radiographs: manual @cite_3 @cite_2 , semi-automatic @cite_24 and in a fully automatic manner @cite_12 @cite_14 @cite_15 . To the best of our knowledge, only the studies by Anthony @cite_12 and Shamir @cite_15 focused on knee joint area localization. While the problem of knee joint area localization can be implicitly solved by annotating the anatomical bone landmarks using deformable models @cite_0 @cite_23 , it would be unfeasible to perform large-scale studies since the use of these algorithms is computationally costly. Currently, despite the presence of multiple approaches, no studies have reported so far their applicability among different datasets. This cross-dataset validation is crucial for the development of clinically applicable solutions.
@cite_14 @cite_3 @cite_24 @cite_0 @cite_23 @cite_2 @cite_15 @cite_12
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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 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. The progression of osteoarthritis (OA) can be monitored by measuring the minimum joint space width (mJSW) between the edges of the femoral condyle and the tibial plateau on radiographs of the knee. This is generally performed by a trained physician using a graduated magnifying lens and is prone to the subjectivity and variation associated with observer measurement. We have developed software that performs this measurement automatically on digitized radiographs. The test data consisted of 180 digitized radiographs of the knee (90 duplicate acquisitions) from 18 normal (nonarthritic) subjects and 38 images from 10 subjects with OA. These were digitized and manually cropped so that the images were free of nonanatomical structures and the knee was approximately centered. The software first determined the edge of the femoral condyle on 400 μm pixel subsampled images. Contours marking the location of the tibial plateau in the medial compartment were found on 100 μm images using the femoral edge as a reference. The algorithm was trained using an independent but similar data set and using a jackknife approach with the test data. The results were compared to contours drawn by a trained reader and the duplicate acquisitions were used to measure the reproducibility of the mJSW measurement. The reproducibility was 0.16 mm and 0.18 mm for normal and osteoarthritic knees, respectively, representing an improvement of approximately a factor of 2 over manual measurement. The algorithm also showed excellent agreement with the hand-drawn contours and with mJSW determined by the manual method.