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Abstract. The aim of the work is to provide a fully automatic method of segmenting vertebrae in spinal radiographs. This is of clinical relevance to the diagnosis of osteoporosis by vertebral fracture assessment, and to grading incident fractures in clinical trials. We use a parts based model of small vertebral patches (e.g. corners). Many potential candidates are found in a global search using multi-resolution normalised correlation. The ambiguity in the possible solution is resolved by applying a graphical model of the connections between parts, and applying geometric constraints. The resulting graph optimisation problem is solved using loopy belief propagation.The minimum cost solution is used to initialise a second phase of active appearance model search. The method is applied to a clinical data set of computed radiography images of lumbar spines. The accuracy of this fully automatic method is assessed by comparing the results to a gold standard of manual annotation by expert radiologists.

The contribution of shape changes to hip osteoarthritis (HOA) remains unclear, as is the extent to which these vary according to HOA severity. In the present study, we used statistical shape modeling (SSM) to evaluate relationships between hip shape and HOA of different severities using UK Biobank DXA images. We performed a cross-sectional study in individuals with left hip dual-energy X-ray absorptiometry (DXA) scans. Statistical shape modeling (SSM) was used to quantify hip shape. Radiographic HOA (rHOA) was classified using osteophyte size and number and joint space narrowing. HOA outcomes ranged in severity from moderate (grade 2) to severe (grade ≥3) rHOA, hospital-diagnosed HOA, and subsequent total hip replacement (THR). Confounder-adjusted logistic regression between the top 10 hip shape modes (HSMs) and OA outcomes was performed. Further models adjusted for alpha angle (AA) and lateral center-edge angle (LCEA), reflecting acetabular dysplasia and cam morphology, respectively. Composite HSM figures were produced combining HSMs associated with separate OA outcomes. A total of 40,311 individuals were included (mean 63.7 years, 47.8% male), of whom 5.7% had grade 2 rHOA, 1.7% grade ≥3 rHOA, 1.3% hospital-diagnosed HOA, and 0.6% underwent THR. Composite HSM figures for grade 2 rHOA revealed femoral neck widening, increased acetabular coverage, and enlarged lesser and greater trochanters. In contrast, grade ≥3 rHOA, hospital-diagnosed HOA, and THR were suggestive of cam morphology and reduced acetabular coverage. Associations between HSMs depicting cam morphology and reduced acetabular coverage and more severe HOA were attenuated by AA and LCEA adjustment, respectively. Relationships between hip shape and HOA differed according to severity. Notably, cam morphology and acetabular dysplasia were features of severe HOA, but unrelated to moderate disease, suggesting possible prognostic utility.

s u m m a r yObjective: To design an automated workflow for hip radiographs focused on joint shape and tests its prognostic value for future hip osteoarthritis. Design: We used baseline and 8-year follow-up data from 1,002 participants of the CHECK-study. The primary outcome was definite radiographic hip osteoarthritis (rHOA) (KellgreneLawrence grade 2 or joint replacement) at 8-year follow-up. We designed a method to automatically segment the hip joint from radiographs. Subsequently, we applied machine learning algorithms (elastic net with automated parameter optimization) to provide the Shape-Score, a single value describing the risk for future rHOA based solely on joint shape. We built and internally validated prediction models using baseline demographics, physical examination, and radiologists scores and tested the added prognostic value of the Shape-Score using Area-Under-the-Curve (AUC). Missing data was imputed by multiple imputation by chained equations. Only hips with pain in the corresponding leg were included. Results: 84% were female, mean age was 56 (±5.1) years, mean BMI 26.3 (±4.2). Of 1,044 hips with pain at baseline and complete follow-up, 143 showed radiographic osteoarthritis and 42 were replaced. 91.5% of the hips had follow-up data available. The Shape-Score was a significant predictor of rHOA (odds ratio per decimal increase 5.21, 95%-CI (3.74e7.24)). The prediction model using demographics, physical examination, and radiologists scores demonstrated an AUC of 0.795, 95%-CI (0.757e0.834). After addition of the Shape-Score the AUC rose to 0.864, 95%-CI (0.833e0.895). Conclusions: Our Shape-Score, automatically derived from radiographs using a novel machine learning workflow, may strongly improve risk prediction in hip osteoarthritis.

Background and purpose — Being able to predict the hip–knee–ankle angle (HKAA) from standard knee radiographs allows studies on malalignment in cohorts lacking full-limb radiography. We aimed to develop an automated image analysis pipeline to measure the femoro-tibial angle (FTA) from standard knee radiographs and test various FTA definitions to predict the HKAA. Patients and methods — We included 110 pairs of standard knee and full-limb radiographs. Automatic search algorithms found anatomic landmarks on standard knee radiographs. Based on these landmarks, the FTA was automatically calculated according to 9 different definitions (6 described in the literature and 3 newly developed). Pearson and intra-class correlation coefficient [ICC]) were determined between the FTA and HKAA as measured on full-limb radiographs. Subsequently, the top 4 FTA definitions were used to predict the HKAA in a 5-fold cross-validation setting. Results — Across all pairs of images, the Pearson correlations between FTA and HKAA ranged between 0.83 and 0.90. The ICC values from 0.83 to 0.90. In the cross-validation experiments to predict the HKAA, these values decreased only minimally. The mean absolute error for the best method to predict the HKAA from standard knee radiographs was 1.8° (SD 1.3). Interpretation — We showed that the HKAA can be automatically predicted from standard knee radiographs with fair accuracy and high correlation compared with the true HKAA. Therefore, this method enables research of the relationship between malalignment and knee pathology in large (epidemiological) studies lacking full-limb radiography.

We have previously described how to model shape variability by means of point distribution models (TDMs,) in which there is a linear relationship between a set of shape parameters and the positions of points on the shape. This linear formulation can fail for shapes which articulate or bend.' we show examples of such failure for both real and synthetic classes of shape. A new, more general formulation for PDMs, based on polynomial regression, is presented. The resulting Polynomial Regression PDMs (PRPDMsj perform well on the data for which the linear method failed.

Hand gesture recognition from video images is of considerable interest as a means of providing simple and intuitive man-machine interfaces. Possible applications range from replacing the mouse as a pointing device to virtual reality and communication with the deaf. We describe an approach to tracking a hand in an image sequence and recognising, in each video frame, which of five gestures it has adopted. A statistically based Point Distribution Model (PDM) is used to provide a compact parameterised description of the shape of the hand for any of the gestures or the transitions between them. The values of the resulting shape parameters are used in a statistical classifier to identify gestures. The model can be used as a deformable template to track a hand through a video sequence but this proves unreliable. We describe how a set of models, one for each of the five gestures, can be used for tracking with the appropriate model selected automatically. We shown that this results in reliable tracking and gesture recognition for two 'unseen' video sequences in which all the gestures are used.