Rethinking Medical Landmark Localization with Prototype Learning-based Progressive Offset Correction

2026-08-10

AI Summary

This work addresses the limitations of existing medical image landmark localization methods, which often incur high computational costs in multi-stage optimization and struggle to balance accuracy with efficiency, particularly in anatomically similar regions. To overcome these challenges, the authors propose the PPOC-LL model, which constructs a multi-scale dynamic-aware feature pyramid and integrates a similarity-driven prototype learning mechanism for offset correction. Additionally, an error-aware reliability regularization is introduced to enhance training stability. The proposed method substantially reduces model parameters while achieving a favorable trade-off between high localization accuracy and low computational complexity across multiple public and private X-ray and ultrasound datasets, thereby significantly improving both robustness and efficiency.

Abstract

Accurate landmark localization in medical images is a fundamental step for quantitative clinical measurement and downstream analysis. Existing localization methods have advanced, among which multi-stage refinement is a superior solution. Although this strategy mitigates the anatomical ambiguity inherent in single-stage global predictions, its high computational cost limits practical applicability. In this work, we propose a parameter-economic model, PPOC-LL, which leverages Prototype learning-based Progressive Offset Correction for Landmark Localization. Our contribution is three-fold. First, to drive coarse-to-fine landmark optimization, we introduce a multi-scale dynamic perception strategy for patch-level feature pyramid modeling. Second, to effectively handle anatomically similar patterns, we design a similarity-driven prototype learning mechanism that captures informative local semantics for robust offset prediction. Last, to stabilize the model learning and improve the overall performance, we incorporate a novel error-aware reliability regularization via tolerance-based balancing. We collected a large validation cohort, including two public and one private datasets spanning X-ray and ultrasound modalities, covering cephalometric, symphysis-fetal head, and fetal heart landmarks. Extensive experiments demonstrate that PPOC-LL achieves satisfactory performance with a favorable trade-off between accuracy and model complexity.

Problem

• medical landmark localization
• anatomical ambiguity
• computational efficiency
• offset correction
• prototype learning

Innovation

• Prototype Learning
• Progressive Offset Correction
• Multi-scale Dynamic Perception
• Error-aware Regularization
• Landmark Localization