Geometric semi-automatic analysis of radiographs of Colles’ fractures

Fractures of the wrist are common in Emergency Departments, where some patients are treated with a procedure called Manipulation under Anaesthesia. In some cases, this procedure is unsuccessful and patients need to revisit the hospital where they undergo surgery to treat the fracture. This work describes a geometric semi-automatic image analysis algorithm to analyse and compare the x-rays of healthy controls and patients with dorsally displaced wrist fractures (Colles’ fractures) who were treated with Manipulation under Anaesthesia. A series of 161 posterior-anterior radiographs from healthy controls and patients with Colles’ fractures were acquired and analysed. The patients’ group was further subdivided according to the outcome of the procedure (successful/unsuccessful) and pre- or post-intervention creating five groups in total (healthy, pre-successful, pre-unsuccessful, post-successful, post-unsuccessful). The semi-automatic analysis consisted of manual location of three landmarks (finger, lunate and radial styloid) and automatic processing to generate 32 geometric and texture measurements, which may be related to conditions such as osteoporosis and swelling of the wrist. Statistical differences were found between patients and controls, as well as between pre- and post-intervention, but not between the procedures. The most distinct measurements were those of texture. Although the study includes a relatively low number of cases and measurements, the statistical differences are encouraging

Radiography Classification: A comparison between Eleven Convolutional Neural Networks

This paper investigates the classification of normal and abnormal radiographic images. Eleven convolutional neural network architectures (GoogleNet, Vgg-19, AlexNet, SqueezeNet, ResNet-18, Inception-v3, ResNet-50, Vgg-16, ResNet-101, DenseNet-201 and Inception-ResNet-v2) were used to classify a series of x-ray images from Stanford Musculoskeletal Radiographs (MURA) dataset corresponding to the wrist images of the data base. For each architecture, the results were compared against the known labels (normal / abnormal) and then the following metrics were calculated: accuracy (labels correctly classified) and Cohen's kappa (a measure of agreement) following MURA guidelines. Numerous experiments were conducted by changing classifiers (Adam, Sgdm, RmsProp), the number of epochs, with/without data augmentation. The best results were provided by InceptionResnet-v2 (Mean accuracy = 0.723, Mean Kappa = 0.506). Interestingly, these results lower than those reported in the Leaderboard of MURA. We speculate that to improve the results from basic CNN architectures several options could be tested, for instance: pre-processing, post-processing or domain knowledge, and ensembles

The Effect of Joint Alignment After a Wrist Injury on Joint Mechanics and Osteoarthritis Development

Wrist injuries are common and can lead to the development of post-traumatic osteoarthritis. For example, one major complication after a wrist fracture, is when the fractured bone heals in a mal-aligned position, called malunion. It has been assumed that a malunion after wrist fractures alters joint congruency and mechanics leading to the development of post-traumatic osteoarthritis and poor functional outcomes. It is unclear whether anatomical restoration is a key component for the management of wrist injuries and to limit the progression of post-traumatic osteoarthritis. However, the mechanistic pathways between joint structure (and mal-alignment) and patient outcomes, such as the development of osteoarthritis and joint function, are not clearly understood due to the limitations in current techniques. The present work advances our understanding of the relationship between joint structure (and mal-alignment) and joint contact mechanics using image-based 3D measurement tools. The purpose of the present work was to employ CT imaging and inter-bone distance mapping to determine the 3D implications of a wrist fracture on 3D joint space area (a measure of joint congruency). This image-based tool was then extended to 4DCT (3DCT and time) to examine the dynamic effects of wrist movement on joint contact mechanics, in the presence of a wrist injury. This research is an important step in the quest to determine a causal relationship between joint structure and patient function

IN VIVO CONTACT MECHANICS OF THE DISTAL RADIOULNAR JOINT WITH AND WITHOUT SCAPHOLUNATE DISSOCIATION

The distal radioulnar joint (DRUJ) is a joint of the wrist which allows force transmission and forearm rotation in the upper limb while preserving the stability of the forearm independent of elbow and wrist flexion and extension. DRUJ is a commonly injured part of the body. Conditions affecting the joint could be positive ulnar variance or negative ulnar variance, the length of the ulna relative to radius. It is also adversely affected by nearby injuries such as distal radial fractures. In fact, a significant correlation was found between negative ulnar variance and scapholunate dissociation (SLD), a ligament injury of the wrist. This leads to the question of whether or not SLD causes changes in the radioulnar joint mechanics. Altered joint mechanics are associated with the onset of osteoarthritis (OA). An understanding of the of the normal and pathological wrist in vivo DRUJ contact mechanics should help physicians make better clinical recommendations and improve treatment for the primary injury. Proper treatment of the DRUJ could help prevent the onset of OA. Image registration is used in our modeling to determine the kinematic transformations for carpal bones from the unloaded to the loaded configuration. A perturbation study was done to evaluate the effect of varying initial manual registrations and the relative image plane orientations on the final registration kinematics. The results of the study showed that Subject II (with different imaging plane orientations) was found to have greater translation errors compared to subject I (consistent imaging planes). This result emphasizes the need to be consistent with forearm position and/or image plane orientation to minimize the errors of translation and attitude vectors. In a separate study, five additional subjects with unilateral SLD participated in another study in which MRI based contact modeling was used to analyze the contact mechanics parameters of the injured wrist compared to the normal wrist. The contact forces, peak contact pressures, average pressures and contact areas generally trended to be higher in injured wrists compared to the normal and surgically repaired wrists. Model contact areas were found to be consistent with the directly measured areas from the grasp MR images. A repeatability test was done on a single subject and the absolute differences between the contact parameters for both the trials were close. These findings suggest that SLD injury of the wrist may have an effect on the DRUJ mechanics

Wrist Fractures Analysis as observed with X-ray imaging

This thesis studies wrist fractures seen on radiographs. Wrist radiographs are anal ysed by two different approaches; first by traditional image processing to extract geometric measurements, then by deep learning to classify risks as normal or abnormal (i.e. fractures or implants). Two data sets are used. The first data set includes wrist radiographs obtained from the Department of Radiology at the University of Exeter. The second data set corresponds to MURA X-ray images (MUsculoskeletal RAdiographs) obtained by the Stanford Machine Learning Team. The MURA data set provides more X-ray images to explore than the first data set. In the first task, a semi-automated geometric image analysis algorithm is proposed to analyse and compare the radiographs of healthy controls and patients with wrist fractures treated by Manipulation under Anaesthesia (MuA). The first dataset was used in this task. Thirty-two geometric and texture measurements were created. Image texture emerged as a metric of the most distinct geometric features from wrist X-rays associated with fractures. In the second task, eleven pre-trained convolutional neural network (CNN) architectures were used. CNN classified the MURA data set into normal and abnormal categories. Transfer learning technique applied to all eleven pre-trained CNNs to deal with wrist X-ray datasets. ResNet-50 and Inception-ResNet-V2 were then explored further using data augmentation strategies. Transfer learning techniques and data augmentation strategies greatly enhance CNN’s ability to classify wrist X-ray images. Class activation mapping (CAM) explores the convolutional neural network’s activation associated with the abnormality within the wrist X-ray image. It shows that CAM can indicate the abnormality area in the wrist’s X-ray image. The graphical heatmap of CAM overlaid on the wrist X-ray image marks the visual point of the area that triggers the CNN’s decision

Mapping Trabecular Bone Fabric Tensor by in Vivo Magnetic Resonance Imaging

The mechanical competence of bone depends upon its quantity, structural arrangement, and chemical composition. Assessment of these factors is important for the evaluation of bone integrity, particularly as the skeleton remodels according to external (e.g. mechanical loading) and internal (e.g. hormonal changes) stimuli. Micro magnetic resonance imaging (µMRI) has emerged as a non-invasive and non-ionizing method well-suited for the repeated measurements necessary for monitoring changes in bone integrity. However, in vivo image-based directional dependence of trabecular bone (TB) has not been linked to mechanical competence or fracture risk despite the existence of convincing ex vivo evidence. The objective of this dissertation research was to develop a means of capturing the directional dependence of TB by assessing a fabric tensor on the basis of in vivo µMRI. To accomplish this objective, a novel approach for calculating the TB fabric tensor based on the spatial autocorrelation function was developed and evaluated in the presence of common limitations to in vivo µMRI. Comparisons were made to the standard technique of mean-intercept-length (MIL). Relative to MIL, ACF was identified as computationally faster by over an order of magnitude and more robust within the range of the resolutions and SNRs achievable in vivo. The potential for improved sensitivity afforded by isotropic resolution was also investigated in an improved µMR imaging protocol at 3T. Measures of reproducibility and reliability indicate the potential of images with isotropic resolution to provide enhanced sensitivity to orientation-dependent measures of TB, however overall reproducibility suffered from the sacrifice in SNR. Finally, the image-derived TB fabric tensor was validated through its relationship with TB mechanical competence in specimen and in vivo µMR images. The inclusion of trabecular bone fabric measures significantly improved the bone volume fraction-based prediction of elastic constants calculated by micro-finite element analysis. This research established a method for detecting TB fabric tensor in vivo and identified the directional dependence of TB as an important determinant of TB mechanical competence