基于原型学习的渐进式偏移校正重新思考医学地标定位
Rethinking Medical Landmark Localization with Prototype Learning-based Progressive Offset Correction
浏览论文内容
中文总结 AI 辅助
本研究提出参数经济的PPOC-LL模型，通过多尺度动态感知、相似性驱动原型学习、误差感知可靠性正则化，在多模态医学地标定位中实现了精度与复杂度的良好平衡。
中文摘要 AI 辅助
医学图像中的准确地标定位是临床定量测量与下游分析的基础步骤。现有定位方法已有进展，其中多阶段精调是一种优越的解决方案，尽管该策略缓解了单阶段全局预测固有的解剖歧义，但其高计算成本限制了实际应用。本研究提出一种参数经济的模型PPOC-LL，其利用基于原型学习的渐进式偏移校正进行地标定位。我们的贡献分为三部分：第一，为实现由粗到细的地标优化，我们引入多尺度动态感知策略用于 patch 级特征金字塔建模；第二，为有效处理解剖学相似模式，我们设计了一种相似性驱动的原型学习机制，以捕获用于鲁棒偏移预测的有用局部语义；第三，为稳定模型学习并提升整体性能，我们纳入了一种基于容忍度平衡的新型感知误差可靠性正则化。我们收集了大型验证队列，包含两个公开数据集和一个私有数据集，覆盖X射线、超声模态，涉及头影测量、胎儿头部联合、胎儿心脏地标。大量实验表明，PPOC-LL在精度与模型复杂度间取得了良好平衡，实现了令人满意的性能。
英文摘要
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.
发表机构
- Shenzhen University（深圳大学）
- Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）
- Boston Children’s Hospital（波士顿儿童医院）
- Harvard Medical School（哈佛医学院）
- School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）
- School of Biomedical Engineering and Informatics, Nanjing Medical University（南京医科大学生物医学工程与信息学院）
- National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学大数据系统计算技术国家工程实验室）
机构由 AI 辅助整理，请以论文原文为准。
