One-shot landmark localization in cephalometric analysis

Deep learning's success in medical image analysis often hinges on large annotated datasets, which are costly and time-consuming to create. This paper proposes One-shot Landmark Detection (OLD) to address this challenge in landmark detection using only a single annotated image. OLD employs a two-stage approach: contrastive learning and pseudo-label supervised training. The former leverages multi-scale feature representations to capture consistent anatomical information, generating predictions for the training set. These predictions then serve as pseudo-labels to train a new landmark detector in the latter stage, further refining performance. Evaluated on a public cephalometric landmark detection dataset, OLD achieves a competitive accuracy of 88.65% within 4.0mm, rivaling state-of-the-art supervised methods trained on significantly larger datasets.