  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.
