AUTHOR=Chen Xu , Fan Xiaochen , Meng Yanda , Zheng Yalin 

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

JOURNAL=Frontiers in Medicine

VOLUME=Volume 11 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2024.1421439

DOI=10.3389/fmed.2024.1421439

ISSN=2296-858X

ABSTRACT=We introduce a novel AI-driven approach to unsupervised fundus image registration utilising our Generalised 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 modelfocused input. We evaluated our model's effectiveness on the publicly available AREDS dataset by using standard metrics such as image-level and parameter-level analyses. 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 analyses, showcases significant improvements in Structural Similarity Index (SSIM) and Normalised 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.