Deep Learning-based Detection of Hip Anatomical Landmarks in X-ray Images.

Objectives: The accurate quantification of hip deformities in medical images presented a significant challenge for radiologists. Inaccurate quantifications can lead to misclassification of diseases and improper treatment. This study introduced two-stage deep learning (DL) models designed for the detection of landmarks in hip X-ray images, aiming to improve the accuracy of deformity quantification. Methods: This study employed two two-stage deep learning (DL) models based on VGG16 and ResNet50. The first stage aimed to detect the bounding box for each landmark, while the second stage focused on pinpointing the exact location of the landmark within the magnified box. The model automatically identified 16 hip landmarks and measures 12 specific dimensions on each side. Training, validation, and testing were conducted on a dataset comprising 854 3-joint lower limb X-ray images depicting various anomalies. Results: The model's measurements were compared with those obtained manually to evaluate performance. The resulting average error ranged from 0.56 to 2.33 mm for different landmarks in the ResNet50-based model. The most accurate measurements were obtained for the femoral head radius and shaft width, with average errors of 0.49 mm and 0.64 mm, respectively. Conversely, the least accurate results were observed for the alpha angle, with an error of 7.37°. Conclusion: This dataset can be valuable for diverse research endeavors related to the hip region. The 2-stage model demonstrated a brief learning time due to the small image size loaded in both steps, while also offering high precision through the use of a bounding box.