# Consistent Point Matching

arXiv: https://arxiv.org/abs/2507.23609 · HTML: https://mlsanity.com/paper/2507.23609/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 31 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images](https://mlsanity.com/paper/2012.02383/) (2020)
- [MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging](https://mlsanity.com/paper/2410.06542/) (2024)
- [Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies](https://mlsanity.com/paper/2012.04872/) (2020)
- [Deep Learning in Medical Image Registration: Magic or Mirage?](https://mlsanity.com/paper/2408.05839/) (2024)
- [SAM++: Enhancing Anatomic Matching using Semantic Information and Structural Inference](https://mlsanity.com/paper/2306.13988/) (2023)
- [A Hierarchical Descriptor Framework for On-the-Fly Anatomical Location Matching between Longitudinal Studies](https://mlsanity.com/paper/2308.07337/) (2023)
- [Real Time Multi Organ Classification on Computed Tomography Images](https://mlsanity.com/paper/2404.18731/) (2024)

## Abstract (arXiv)

This study demonstrates that incorporating a consistency heuristic into the point-matching algorithm citeyerebakan2023hierarchical 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.