AI-driven generalized polynomial transformation models for unsupervised fundus image registration

Xu Chen, Xiaochen Fan, Yanda Meng, Yalin Zheng
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Abstract

We introduce a novel AI-driven approach to unsupervised fundus image registration utilizing our Generalized Polynomial Transformation (GPT) model. Through the GPT, we establish a foundational model capable of simulating diverse polynomial transformations, trained on a large synthetic dataset to encompass a broad range of transformation scenarios. Additionally, our hybrid pre-processing strategy aims to streamline the learning process by offering model-focused input. We evaluated our model's effectiveness on the publicly available AREDS dataset by using standard metrics such as image-level and parameter-level analyzes. Linear regression analysis reveals an average Pearson correlation coefficient (R) of 0.9876 across all quadratic transformation parameters. Image-level evaluation, comprising qualitative and quantitative analyzes, showcases significant improvements in Structural Similarity Index (SSIM) and Normalized Cross Correlation (NCC) scores, indicating its robust performance. Notably, precise matching of the optic disc and vessel locations with minimal global distortion are observed. These findings underscore the potential of GPT-based approaches in image registration methodologies, promising advancements in diagnosis, treatment planning, and disease monitoring in ophthalmology and beyond.
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用于无监督眼底图像配准的人工智能驱动广义多项式变换模型
我们利用广义多项式变换(GPT)模型,为无监督眼底图像配准引入了一种新颖的人工智能驱动方法。通过 GPT,我们建立了一个能够模拟多种多项式变换的基础模型,并在一个大型合成数据集上进行了训练,以涵盖广泛的变换场景。此外,我们的混合预处理策略旨在通过提供以模型为中心的输入来简化学习过程。我们通过图像级和参数级分析等标准指标,评估了我们的模型在公开的 AREDS 数据集上的有效性。线性回归分析显示,所有二次变换参数的平均皮尔逊相关系数 (R) 为 0.9876。由定性和定量分析组成的图像级评估显示,结构相似性指数(SSIM)和归一化交叉相关性(NCC)得分均有显著提高,表明其性能强劲。值得注意的是,它能精确匹配视盘和血管的位置,并将全局失真降到最低。这些发现凸显了基于 GPT 的图像配准方法的潜力,有望推动眼科及其他领域的诊断、治疗规划和疾病监测。
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