通过一致性启发式提升医学图像配准鲁棒性，无需训练即可高精度导航。
Consistent Point Matching
- 引入一致性约束优化点匹配算法，增强跨模态图像配准稳定性。
- 在Deep Lesion Tracking数据集上超越现有最佳结果，定位准确率显著提升。
- 仅需普通CPU即可运行，支持速度与鲁棒性的灵活权衡，适合临床部署。
本研究证明，在点匹配算法中引入一致性启发式可显著提升跨对医学图像解剖位置匹配的鲁棒性。我们在涵盖CT和MRI模态的多种纵向内部及公开数据集上验证了该方法。值得注意的是，其在Deep Lesion Tracking数据集上的表现优于现有最先进方法。此外，该方法有效解决了关键点定位问题。算法在标准CPU硬件上运行高效，支持速度与鲁棒性之间的可配置权衡。该方法无需机器学习模型或训练数据，即可实现医学图像间的高精度导航。
原文摘要 · Abstract (English)
This study demonstrates that incorporating a consistency heuristic into the point-matching algorithm \cite{yerebakan2023hierarchical} improves robustness in matching anatomical locations across pairs of medical images. We validated our approach on diverse longitudinal internal and public datasets spanning CT and MRI modalities. Notably, it surpasses state-of-the-art results on the Deep Lesion Tracking dataset. Additionally, we show that the method effectively addresses landmark localization. The algorithm operates efficiently on standard CPU hardware and allows configurable trade-offs between speed and robustness. The method enables high-precision navigation between medical images without requiring a machine learning model or training data.
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