Correction of Two Human Key-Points Estimations for Medical Applications

Luo H.; Rashid S.R.; Nie Y.; Chen A.; Li C.; Wu S.; Zheng Z.; Cheng Z.; Castaneda B.; Peng B.; Tian C.; Wu Z. · 2023 IEEE 6th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023 (2023) · DOI: 10.1109/prai59366.2023.10331970

Resumen

Anatomical key-points recognition is essential in many medical image analyses and clinical healthcare applications. Successfully identifying these anatomical key points provides multiple advantages, such as assisting medical experts in making treatment adjustments and offering information that helps to position surgical instruments at the appropriate locations. However, manual anatomical key-point recognition is subjective, slow, and time-consuming, especially when processing many medical images in clinical institutions. To overcome these limitations, this study aims to establish the correlation between human anatomical key points based on OpenPose and Baidu AI key-point detection techniques and the truth ground anatomical key-points marked by therapists in human medical images. This relationship will help to optimize the detection performance, reduce cost, decrease human error, and accelerate the process. The Sichuan Cancer Hospital provided five whole-body scan images obtained from a clinical CT scanner. A medical expert subsequently identified 14 anatomical key points from each scan. Finally, the datasets were reconstructed into 3-dimensional volume models to visualize whole-body skin models and the skeletons. The human-Annotated 14 key points were then used as ground truth compared to the computer vision techniques: OpenPose and Baidu AI. Both OpenPose and Baidu AI were found to have systematic offsets from the ground true reference points. These findings are reported in this work and can be used as a correction method.

Instituciones: Pontificia Universidad Católica del Perú