Sparse Image Registration-Based Marker-Free Hand Acupoint Localization Using TCM Template Queries.

Deep learning for sensor-based medical imaging provides a non-contact and data-driven route for anatomical surface analysis and personalized traditional Chinese medicine (TCM) applications. However, accurate hand-acupoint localization from camera-acquired hand images remains challenging because expert-annotated acupoint datasets are limited and inter-subject anatomical variations are significant. This paper proposes a marker-free hand-acupoint localization method based on deep feature correspondence learning and sparse image registration. The task is formulated as template-to-target correspondence estimation, in which expert-annotated acupoints in a TCM template image are used as query points and mapped to a target hand image acquired by an optical imaging sensor. A Transformer-based architecture is employed to correlate multi-scale image features, and an uncertainty-aware matching formulation is used to estimate both acupoint positions and unreliable matches. Unlike conventional keypoint detection networks, the proposed method exploits TCM template priors and reduces the dependence on dense target-image acupoint annotations. The constructed dataset contains 1400 images from 378 participants. Participant-level partitioning was performed before image-pair generation, yielding a held-out test set of 38 participants (140 images). Palm and dorsal-hand views were evaluated separately against keypoint-detection baselines and COTR. On this participant-independent internal test set, the proposed method achieved AAPE values of 18.42 pixels for palm images and 12.60 pixels for dorsal-hand images, while reducing inference time from 11,000 ms for COTR to 600 ms. These results indicate the feasibility of template-guided image-based hand-acupoint localization with reference to expert annotations.