RealKeyMorph: Keypoints in Real-world Coordinates for Resolution-agnostic Image Registration
Mina C. Moghadam, Alan Q. Wang, Omer Taub, Martin R. Prince, Mert R. Sabuncu

TL;DR
RealKeyMorph (RKM) is a novel resolution-agnostic image registration method that directly predicts keypoints in real-world coordinates, avoiding resampling artifacts and improving registration across varying image resolutions.
Contribution
RKM extends KeyMorph by operating in real-world coordinates, enabling resolution-agnostic registration without resampling, applicable to 2D and 3D medical images.
Findings
RKM outperforms resampling-based methods in registration accuracy.
Effective on both 2D abdominal MRI stacks and 3D brain datasets.
Demonstrates robustness across different resolutions.
Abstract
Many real-world settings require registration of a pair of medical images that differ in spatial resolution, which may arise from differences in image acquisition parameters like pixel spacing, slice thickness, and field-of-view. However, all previous machine learning-based registration techniques resample images onto a fixed resolution. This is suboptimal because resampling can introduce artifacts due to interpolation. To address this, we present RealKeyMorph (RKM), a resolution-agnostic method for image registration. RKM is an extension of KeyMorph, a registration framework which works by training a network to learn corresponding keypoints for a given pair of images, after which a closed-form keypoint matching step is used to derive the transformation that aligns them. To avoid resampling and enable operating on the raw data, RKM outputs keypoints in real-world coordinates of the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMedical Image Segmentation Techniques · Advanced Image and Video Retrieval Techniques · Robotics and Sensor-Based Localization
