SCS: Spatially Consistent Self-Supervised approach for One-Shot Anatomical Landmark Detection

Abstract

Landmark detection is essential in medical image analysis, serving as the foundation for many downstream tasks. In recent years, supervised anatomical landmark detection models have achieved remarkable success, but typically require large amounts of labeled data for training, which is challenging to obtain due to the expertise and time needed for accurate annotation. Rather than relying on costly expert annotations, this paper focuses on leveraging a one-shot method for automated annotation. To this end, we propose a Spatially Consistent Self-supervised approach (SCS) within a two-stage framework for one-shot anatomical landmark detection. In the first stage, we design a multi-scale contrastive self-supervised method that leverages the inherent spatial consistency of medical images, characterized by clear structures and similar patterns, to extract global and local features. During inference, pseudo-labels are generated based on the one-shot template. In the second stage, we train a supervised model using the pseudo-labels and mitigate label noise through a mask and multi-task approach. Our method is evaluated on three widely-used public X-ray datasets, achieving state-of-the-art performance across almost all metrics with the Mean Radial Error (MRE) reduced to 1.97mm on the Cephalometric dataset, 1.43mm on the Hand dataset, and 6.58mm on the Chest dataset, thereby demonstrating the effectiveness of our Spatially Consistent Self-supervised approach.