Work

Multi-modal and inter-device retinal OCT registration via learning-based feature correspondence

Year: 2026

Type: article

Abstract: Accurate registration of widefield OCT volumes is essential in ophthalmology to compensate for motion artifacts, enhance image quality, and enable precise delineation of retinal structures for both single- and multi-modal imaging. We present a learning-guided, feature-based 3D registration framework that combines advanced keypoint detection, transformer-based feature matching, and non-rigid warping to align multiple OCT volumes across repeated acquisitions, encompassing both healthy and diseased cases. Our approach supports both longitudinal, inter-device, and multi-modal registration, including alignment between OCT and standard color fundus images. At a single time point, registered volumes are averaged to produce high-quality, motion-free representations with enhanced signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and high structural similarity index (SSIM), while the derived transformations can be applied to polarization-resolved signals to extract additional functional information. Quantitative evaluation demonstrates substantial improvements in SNR and CNR, with a mean 3D SSIM exceeding 0.97 for single modality cases, and landmark-based target registration error reduced to 2 pixels for multi-modal cases, indicating precise geometric alignment. Qualitatively, the method reduces speckle noise, improves layer connectivity, and strengthens retinal vascular contrast, providing clinicians with a more reliable baseline for interpretation. This framework is versatile, applicable to multi-modal OCT analysis, and enables downstream tasks such as segmentation, quantitative assessment, and longitudinal studies, offering significant potential for enhanced retinal disease monitoring and clinical decision-making.

Source: Biomedical Optics Express

DOI: 10.1364/boe.596169