  One-shot detection of anatomical landmarks is gaining significant attention
for its efficiency in using minimal labeled data to produce promising results.
However, the success of current methods heavily relies on the employment of
extensive unlabeled data to pre-train an effective feature extractor, which
limits their applicability in scenarios where a substantial amount of unlabeled
data is unavailable. In this paper, we propose the first foundation
model-enabled one-shot landmark detection (FM-OSD) framework for accurate
landmark detection in medical images by utilizing solely a single template
image without any additional unlabeled data. Specifically, we use the frozen
image encoder of visual foundation models as the feature extractor, and
introduce dual-branch global and local feature decoders to increase the
resolution of extracted features in a coarse to fine manner. The introduced
feature decoders are efficiently trained with a distance-aware similarity
learning loss to incorporate domain knowledge from the single template image.
Moreover, a novel bidirectional matching strategy is developed to improve both
robustness and accuracy of landmark detection in the case of scattered
similarity map obtained by foundation models. We validate our method on two
public anatomical landmark detection datasets. By using solely a single
template image, our method demonstrates significant superiority over strong
state-of-the-art one-shot landmark detection methods.
