One-shot landmark localization in cephalometric analysis

Tianbiao Luo; Yang Zhang; Honglin Xiang; Jiahao Wang

doi:10.1117/12.3040496

10 September 2024 One-shot landmark localization in cephalometric analysis

Tianbiao Luo, Yang Zhang, Honglin Xiang, Jiahao Wang

Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 1325717 (2024) https://doi.org/10.1117/12.3040496
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tianbiao Luo, Yang Zhang, Honglin Xiang, and Jiahao Wang "One-shot landmark localization in cephalometric analysis", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 1325717 (10 September 2024); https://doi.org/10.1117/12.3040496

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KEYWORDS

Education and training

Anatomy

Machine learning

Medical imaging

Sensors

Data modeling

Image enhancement

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