A Dataset and Method for Hallux Valgus Angle Estimation Based on Deep Learing
This addresses a domain-specific problem for medical professionals dealing with forefoot deformities, but it is incremental as it adapts existing methods to a new dataset.
The paper tackled the problem of automating Hallux Valgus angle measurement, which is time-consuming and unreliable when done manually, by creating a dataset and developing a deep learning algorithm with linear regression that shows great fitting ability to ground truth.
Angular measurements is essential to make a resonable treatment for Hallux valgus (HV), a common forefoot deformity. However, it still depends on manual labeling and measurement, which is time-consuming and sometimes unreliable. Automating this process is a thing of concern. However, it lack of dataset and the keypoints based method which made a great success in pose estimation is not suitable for this field.To solve the problems, we made a dataset and developed an algorithm based on deep learning and linear regression. It shows great fitting ability to the ground truth.