Ultra-wide field and new wide field composite retinal image registration with AI-enabled pipeline and 3D distortion correction algorithm

• Published in: Eye (London) Vol. 38; no. 6; pp. 1189 - 1195
• Main Authors: Kalaw, Fritz Gerald P., Cavichini, Melina, Zhang, Junkang, Wen, Bo, Lin, Andrew C., Heinke, Anna, Nguyen, Truong, An, Cheolhong, Bartsch, Dirk-Uwe G., Cheng, Lingyun, Freeman, William R.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.04.2024; Nature Publishing Group
• Subjects: 59; 692/698; 692/700/1421; 706/648/160; Algorithms; Artificial intelligence; Blood vessels; Laboratory Medicine; Medicine; Medicine & Public Health; Neural networks; Ophthalmology; Pharmaceutical Sciences/Technology; Retina; Surgery; Surgical Oncology
• ISSN: 0950-222X; 1476-5454
• DOI: 10.1038/s41433-023-02868-3

Purpose This study aimed to compare a new Artificial Intelligence (AI) method to conventional mathematical warping in accurately overlaying peripheral retinal vessels from two different imaging devices: confocal scanning laser ophthalmoscope (cSLO) wide-field images and SLO ultra-wide field images. Methods Images were captured using the Heidelberg Spectralis 55-degree field-of-view and Optos ultra-wide field. The conventional mathematical warping was performed using Random Sample Consensus—Sample and Consensus sets (RANSAC-SC). This was compared to an AI alignment algorithm based on a one-way forward registration procedure consisting of full Convolutional Neural Networks (CNNs) with Outlier Rejection (OR CNN), as well as an iterative 3D camera pose optimization process (OR CNN + Distortion Correction [DC]). Images were provided in a checkerboard pattern, and peripheral vessels were graded in four quadrants based on alignment to the adjacent box. Results A total of 660 boxes were analysed from 55 eyes. Dice scores were compared between the three methods (RANSAC-SC/OR CNN/OR CNN + DC): 0.3341/0.4665/4784 for fold 1-2 and 0.3315/0.4494/4596 for fold 2-1 in composite images. The images composed using the OR CNN + DC have a median rating of 4 (out of 5) versus 2 using RANSAC-SC. The odds of getting a higher grading level are 4.8 times higher using our OR CNN + DC than RANSAC-SC ( p  < 0.0001). Conclusion Peripheral retinal vessel alignment performed better using our AI algorithm than RANSAC-SC. This may help improve co-localizing retinal anatomy and pathology with our algorithm.