Chen Xu, Fan Xiaochen, Meng Yanda, Zheng Yalin
Department of Medicine, University of Cambridge, Cambridge, United Kingdom.
Institute of Ophthalmology, University College London, London, United Kingdom.
Front Med (Lausanne). 2024 Jul 16;11:1421439. doi: 10.3389/fmed.2024.1421439. eCollection 2024.
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.
我们介绍了一种新颖的人工智能驱动方法，用于利用我们的广义多项式变换（GPT）模型进行无监督眼底图像配准。通过GPT，我们建立了一个能够模拟各种多项式变换的基础模型，该模型在大型合成数据集上进行训练，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重点的输入来简化学习过程。我们使用图像级和参数级分析等标准指标，在公开可用的AREDS数据集上评估了我们模型的有效性。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。包括定性和定量分析的图像级评估显示，结构相似性指数（SSIM）和归一化互相关（NCC）分数有显著提高，表明其性能稳健。值得注意的是，观察到视盘和血管位置精确匹配，全局失真最小。这些发现强调了基于GPT的方法在图像配准方法中的潜力，有望在眼科及其他领域的诊断、治疗规划和疾病监测方面取得进展。
