Consistent Point Matching
This work addresses the need for high-precision navigation between medical images without requiring machine learning models or training data, though it is incremental as it builds on an existing algorithm.
The study tackled the problem of matching anatomical locations across medical images by incorporating a consistency heuristic into a point-matching algorithm, resulting in improved robustness and surpassing state-of-the-art results on the Deep Lesion Tracking dataset.
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.