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.
Finally, it should be mentioned, that the problem of joint area localization is not limited to only knee radiographs. The attention of the research community is also focused on hand radiographs, where OA occurs as well. It has been recently shown how the anatomic structure of the image can be utilized to annotate hand radiographic images @cite_16 . However, to the best of authors' knowledge, there are no studies in the knee domain where such information is used to segment or annotate the image. In this study, we show how such information can be used to annotate knee radiographs.
@cite_16
2584226844 2169734502 2006609734 2521048164
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 equals to: 0.84, 0.79 and 0.78. Using a high-end computer, the method allows to automatically annotate conventional knee radiographs within 14–16 ms and high resolution ones within 170 ms. Our results demonstrate that the developed method is suitable for large-scale analyses. 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. 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. This paper proposes a new approach to automatically quantify the severity of knee osteoarthritis (OA) from radiographs using deep convolutional neural networks (CNN). Clinically, knee OA severity is assessed using Kellgren & Lawrence (KL) grades, a five point scale. Previous work on automatically predicting KL grades from radiograph images were based on training shallow classifiers using a variety of hand engineered features. We demonstrate that classification accuracy can be significantly improved using deep convolutional neural network models pre-trained on ImageNet and fine-tuned on knee OA images. Furthermore, we argue that it is more appropriate to assess the accuracy of automatic knee OA severity predictions using a continuous distance-based evaluation metric like mean squared error than it is to use classification accuracy. This leads to the formulation of the prediction of KL grades as a regression problem and further improves accuracy. Results on a dataset of X-ray images and KL grades from the Osteoarthritis Initiative (OAI) show a sizable improvement over the current state-of-the-art.