Gaussian Primitive Optimized Deformable Retinal Image Registration

• URL: http://arxiv.org/abs/2508.16852v1
• Date: Sat, 23 Aug 2025 00:44:50 GMT
• Title: Gaussian Primitive Optimized Deformable Retinal Image Registration
• Authors: Xin Tian, Jiazheng Wang, Yuxi Zhang, Xiang Chen, Renjiu Hu, Gaolei Li, Min Liu, Hang Zhang,
• Abstract summary: Deformable retinal image registration is notoriously difficult due to large homogeneous regions and sparse but critical vascular features.<n>We introduce a novel iterative framework that performs structured message passing to overcome these challenges.<n>Experiments on the FIRE dataset show that GPO reduces the target registration error from 6.2,px to 2.4,px and increases the AUC at 25,px from 0.770 to 0.938.
• Score: 19.882820812725523
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deformable retinal image registration is notoriously difficult due to large homogeneous regions and sparse but critical vascular features, which cause limited gradient signals in standard learning-based frameworks. In this paper, we introduce Gaussian Primitive Optimization (GPO), a novel iterative framework that performs structured message passing to overcome these challenges. After an initial coarse alignment, we extract keypoints at salient anatomical structures (e.g., major vessels) to serve as a minimal set of descriptor-based control nodes (DCN). Each node is modelled as a Gaussian primitive with trainable position, displacement, and radius, thus adapting its spatial influence to local deformation scales. A K-Nearest Neighbors (KNN) Gaussian interpolation then blends and propagates displacement signals from these information-rich nodes to construct a globally coherent displacement field; focusing interpolation on the top (K) neighbors reduces computational overhead while preserving local detail. By strategically anchoring nodes in high-gradient regions, GPO ensures robust gradient flow, mitigating vanishing gradient signal in textureless areas. The framework is optimized end-to-end via a multi-term loss that enforces both keypoint consistency and intensity alignment. Experiments on the FIRE dataset show that GPO reduces the target registration error from 6.2\,px to ~2.4\,px and increases the AUC at 25\,px from 0.770 to 0.938, substantially outperforming existing methods. The source code can be accessed via https://github.com/xintian-99/GPOreg.

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