Detection of human lower limb mechanical axis key points and its application on patella misalignment detection

The human lower limb mechanical axis is the most basic and essential diagnosis reference in clinical orthopedics. Orthopedists diagnose the varus or valgus knee according to the status of the lower limb mechanical axis. The conventional method used in this task relies on manual measurement, which is time-consuming and has operational differences. Given the above reason, in this work, we focus on designing a deep learning algorithm to address this problem and present a novel convolutional neural network architecture for mechanical axis detection. After the mechanical axis is detected, HKAA (Hip-Knee-Ankle Angle), which is a medical index, can be calculated automatically to assist in the medical diagnosis. We locate the mechanical axis by detecting both ends’ key points. Then we apply the detected key points to implement the patella misalignment detection for auxiliary radiography imaging. The mechanical axis key points detection network is based on the stacked hourglass module and adopts the deformable convolution for modeling the geometric features. Besides, we introduce an offset branch to reduce the systematic error. Then a detector trained in a semi-supervised strategy is applied for patella detection. The horizontal deviation of the patella from the knee center reflects the alignment of the patella. We use 879 collected radiographs (X-ray images) to train the key point detection model and other 98 radiographs perform as the validation set in this study. The proposed model achieves an accuracy of 83.0% for key points and reaches 61.1 mAP in patella detection. This model achieves excellent performance in human lower limb mechanical axis and patella detection.