CNN-based Pose Estimation to Assist Medical Imaging

The correct execution of scanning protocols is crucial to ensure the quality of radiographic examinations. Recent advances in machine learning methods have opened up new possibilities for medical imaging. However, the potential of pose estimation models in this field is still largely unexplored. This study aims to address this gap by investigating the performance and an application of pose estimation in the context of X-ray image acquisition. To this goal, a pose estimation model was selected from a pool of state-of-the-art models. It was then trained on a dataset of 213 images of humans undergoing X-ray imaging. Despite the limited size of the dataset, the model achieved an AP of 0.902 and a near real-time inference speed of 7 FPS on CPU. The detection of landmarks through pose estimation enables the automatic assessment of pose adherence to prescribed imaging protocols. This automation can reduce human errors and alleviate the mental workload on radiologists. The results of this study highlight the potential of convolutional neural network-based pose estimation models to assist radiologists in performing X-ray imaging tasks effectively.