Accurate image registration is pivotal in biomedical image analysis, where selecting suitable registration algorithms demands careful consideration. While numerous algorithms are available, the evaluation metrics to assess their performance have remained relatively static. This study addresses this challenge by introducing a novel evaluation metric termed Landmark Hit Rate (HitR), which focuses on the clinical relevance of image registration accuracy. Unlike traditional metrics such as Target Registration Error, which emphasize subresolution differences, HitR considers whether registration algorithms successfully position landmarks within defined confidence zones. This paradigm shift acknowledges the inherent annotation noise in medical images, allowing for more meaningful assessments. To equip HitR with label-noise-awareness, we propose defining these confidence zones based on an Inter-rater Variance analysis. Consequently, hit rate curves are computed for varying landmark zone sizes, enabling performance measurement for a task-specific level of accuracy. Our approach offers a more realistic and meaningful assessment of image registration algorithms, reflecting their suitability for clinical and biomedical applications.

翻译：精确的图像配准在生物医学图像分析中至关重要，其中选择合适的配准算法需要仔细考量。尽管已有众多算法可用，但评估其性能的指标却相对固定。本研究通过引入一种称为界标命中率（HitR）的新型评估指标来应对这一挑战，该指标聚焦于图像配准精度的临床相关性。与强调亚分辨率差异的传统指标（如目标配准误差）不同，HitR关注配准算法是否成功将界标定位在定义的置信区域内。这一范式转变承认了医学图像中固有的标注噪声，从而允许进行更有意义的评估。为使HitR具备标签噪声感知能力，我们提出基于评分者间方差分析来定义这些置信区域。因此，可针对不同界标区域尺寸计算命中率曲线，从而实现对特定任务精度水平的性能度量。我们的方法为图像配准算法提供了更现实且更有意义的评估，反映了其在临床和生物医学应用中的适用性。