Diffusion data augmentation for enhancing Norberg hip angle estimation

The Norberg angle (NA) plays a crucial role in evaluating hip joint conformation, particularly in canines, by quantifying femoral head subluxation within the hip joint. Therefore, it is an important metric for evaluating hip joint quality and diagnosing canine hip dysplasia, the most prevalent hereditary orthopedic disorder in dogs. While contemporary tools offer automated quantification of the NA, their usage typically entails manual labeling and verification of radiographic images by professional veterinarians. To enhance efficiency and streamline this process, the study aims to develop a tool capable of predicting the NA directly from the image without the need for veterinary intervention. Due to the challenges in acquiring annotated, diverse, high-quality images, this study introduces diffusion models to expand the training dataset from 219 to 1493 images, encompassing original images. This augmentation enhances the dataset's diversity and scale, thereby improving the accuracy of Norberg angle estimation. The model predicts four key points: the center of left and right femoral heads and the edge of the left and right acetabulum, as well as the radii of the femoral heads and the Norberg angles. By evaluating 18 distinct pretrained ImageNet models, we investigate their performance pre- and post-incorporating augmented data from generated images. The results demonstrate a significant enhancement, with an average 35.3% improvement based on mean absolute percentage error when utilizing generated images from diffusion models. This study showcases the potential of diffusion modeling in advancing canine hip dysplasia diagnosis and underscores the value of incorporating augmented data to elevate model accuracy.