Quantifying the characteristics of the acetabulum and proximal femur using a semi-automated hip morphology software programme (SHIPS)

Development of Local Software for Automatic Measurement of Geometric Parameters in the Proximal Femur Using a Combination of a Deep Learning Approach and an Active Shape Model on X-ray Images

Proximal femur geometry is an important risk factor for diagnosing and predicting hip and femur injuries. Hence, the development of an automated approach for measuring these parameters could help physicians with the early identification of hip and femur ailments. This paper presents a technique that combines the active shape model (ASM) and deep learning methodologies. First, the femur boundary is extracted by a deep learning neural network. Then, the femur's anatomical landmarks are fitted to the extracted border using the ASM method. Finally, the geometric parameters of the proximal femur, including femur neck axis length (FNAL), femur head diameter (FHD), femur neck width (FNW), shaft width (SW), neck shaft angle (NSA), and alpha angle (AA), are calculated by measuring the distances and angles between the landmarks. The dataset of hip radiographic images consisted of 428 images, with 208 men and 220 women. These images were split into training and testing sets for analysis. The deep learning network and ASM were subsequently trained on the training dataset. In the testing dataset, the automatic measurement of FNAL, FHD, FNW, SW, NSA, and AA parameters resulted in mean errors of 1.19%, 1.46%, 2.28%, 2.43%, 1.95%, and 4.53%, respectively.

Automated measurement of alpha angle on 3D-magnetic resonance imaging in femoroacetabular impingement hips: a pilot study

BACKGROUND

Femoroacetabular impingement (FAI) syndrome is an established pre-osteoarthritic condition. Diagnosis is based on both clinical and radiographic parameters. An abnormal manually calculated alpha angle in magnetic resonance imaging (MRI) is traditionally utilized to diagnose abnormal femoral head-neck offset. This pilot study aimed to assess the feasibility of automated alpha angle measurements in patients with FAI syndrome, and to compare automated with manual measurements data with regard to the time and effort needed in each method.

METHODS

Alpha angles were measured with manual and automated techniques, using postprocessing software in nineteen hip MRIs of FAI syndrome patients. Two observers conducted manual measurements. Intra- and inter-observer reproducibility and correlation of manual and automated alpha angle measurements were calculated using intra-class correlation (ICC) analysis. Both techniques were compared regarding the time taken (in minutes) and effort required, measured as the amount of mouse button presses performed.

RESULTS

The first observer's intra-observer reproducibility was good (ICC 0.77; p < 0.001) while the second observer's was good-to-excellent (ICC 0.93; p < 0.001). Inter-observer reproducibility between both observers in the first (ICC 0.45; p < 0.001) and second (ICC 0.56; p < 0.001) manual alpha angle assessment was moderate. The intra-class correlation coefficients between manual and automated alpha angle measurements were ICC = 0.24 (p = 0.052; observer 1, 1st measurement), ICC = 0.32 (p = 0.015; observer 1, 2nd measurement), ICC = 0.50 (p < 0.001; observer 2, 1st measurement), and ICC = 0.45 (p < 0.001; observer 2, 2nd measurement). Average runtime for automatic processing of the image data for the automated assessment was 16.6 ± 1.9 min. Automatic alpha angle measurements took longer (time difference: 14.6 ± 3.9 min; p < 0.001) but required less effort (difference in button presses: 231 ± 23; p < 0.001). While the automatic processing is running, the user can perform other tasks.

CONCLUSIONS

This pilot study demonstrates that objective and reliable automated alpha angle measurement of MRIs in FAI syndrome hips is feasible. Trial registration The Ethics Committee of the University of Düsseldorf approved our study (Registry-ID: 2017084398).

Prevalence of radiographic hip dysplasia in Japanese population-based study

OBJECTIVES

The purpose of this study was to measure the indices of radiographic developmental dysplasia of the hip (DDH) in a cross-sectional study of an elderly Japanese population.

METHODS

Hip radiographs of 427 informed, voluntary Japanese community-dwelling individuals (279 female and 148 male) aged 50-96 years-old were obtained from Miyagawa village in Japan through a health screening. The hip radiographs were measured by a custom-written, semi-automated MATLAB program. The center edge (CE) angle, acetabular roof obliquity (ARO), acetabular head index (AHI), and minimum joint space width (mJSW) were measured. We examined the associations between gender, side-of-hip, and age group on radiographic DDH and hip osteoarthritis (OA).

RESULTS

The mean CE angle was 31.0°. The mean ARO was 5.8°. The mean AHI was 88.2%. The mean mJSW was 4.0 mm. Of the total population, 29.9% had DDH and 4.0% had hip OA. Of those who had hip OA, 41.2% were secondary OA, and 58.8% were primary OA. The relationship between DDH and OA was not significant.

CONCLUSION

DDH is unlikely to be an important cause of hip OA in the present population-based study.

Development of a fully automatic shape model matching (FASMM) system to derive statistical shape models from radiographs: application to the accurate capture and global representation of proximal femur shape

OBJECTIVE

To evaluate the accuracy and sensitivity of a fully automatic shape model matching (FASMM) system to derive statistical shape models (SSMs) of the proximal femur from non-standardised anteroposterior (AP) pelvic radiographs.

DESIGN

AP pelvic radiographs obtained with informed consent and appropriate ethical approval were available for 1105 subjects with unilateral hip osteoarthritis (OA) who had been recruited previously for The arcOGEN Study. The FASMM system was applied to capture the shape of the unaffected (i.e., without signs of radiographic OA) proximal femur from these radiographs. The accuracy and sensitivity of the FASMM system in calculating geometric measurements of the proximal femur and in shape representation were evaluated relative to validated manual methods.

RESULTS

De novo application of the FASMM system had a mean point-to-curve error of less than 0.9 mm in 99% of images (n = 266). Geometric measurements generated by the FASMM system were as accurate as those obtained manually. The analysis of the SSMs generated by the FASMM system for male and female subject groups identified more significant differences (in five of 17 SSM modes after Bonferroni adjustment) in their global proximal femur shape than those obtained from the analysis of conventional geometric measurements. Multivariate gender-classification accuracy was higher when using SSM mode values (76.3%) than when using conventional hip geometric measurements (71.8%).

CONCLUSIONS

The FASMM system rapidly and accurately generates a global SSM of the proximal femur from radiographs of varying quality and resolution. This system will facilitate complex morphometric analysis of global shape variation across large datasets. The FASMM system could be adapted to generate SSMs from the radiographs of other skeletal structures such as the hand, knee or pelvis.