Choroidalyzer: An Open-Source, End-to-End Pipeline for Choroidal Analysis in Optical Coherence Tomography

• Published in: Investigative ophthalmology & visual science Vol. 65; no. 6; p. 6
• Main Authors: Engelmann, Justin, Burke, Jamie, Hamid, Charlene, Reid-Schachter, Megan, Pugh, Dan, Dhaun, Neeraj, Moukaddem, Diana, Gray, Lyle, Strang, Niall, McGraw, Paul, Storkey, Amos, Steptoe, Paul J., King, Stuart, MacGillivray, Tom, Bernabeu, Miguel O., MacCormick, Ian J. C.
• Format: Journal Article
• Language: English
• Published: United States The Association for Research in Vision and Ophthalmology 04.06.2024
• Subjects: Adult; Aged; Choroid - blood supply; Choroid - diagnostic imaging; Deep Learning; Female; Fovea Centralis - blood supply; Fovea Centralis - diagnostic imaging; Humans; Male; Middle Aged; Multidisciplinary Ophthalmic Imaging; Reproducibility of Results; Retinal Vessels - diagnostic imaging; Tomography, Optical Coherence - methods
• ISSN: 1552-5783; 0146-0404; 1552-5783
• DOI: 10.1167/iovs.65.6.6

To develop Choroidalyzer, an open-source, end-to-end pipeline for segmenting the choroid region, vessels, and fovea, and deriving choroidal thickness, area, and vascular index. We used 5600 OCT B-scans (233 subjects, six systemic disease cohorts, three device types, two manufacturers). To generate region and vessel ground-truths, we used state-of-the-art automatic methods following manual correction of inaccurate segmentations, with foveal positions manually annotated. We trained a U-Net deep learning model to detect the region, vessels, and fovea to calculate choroid thickness, area, and vascular index in a fovea-centered region of interest. We analyzed segmentation agreement (AUC, Dice) and choroid metrics agreement (Pearson, Spearman, mean absolute error [MAE]) in internal and external test sets. We compared Choroidalyzer to two manual graders on a small subset of external test images and examined cases of high error. Choroidalyzer took 0.299 seconds per image on a standard laptop and achieved excellent region (Dice: internal 0.9789, external 0.9749), very good vessel segmentation performance (Dice: internal 0.8817, external 0.8703), and excellent fovea location prediction (MAE: internal 3.9 pixels, external 3.4 pixels). For thickness, area, and vascular index, Pearson correlations were 0.9754, 0.9815, and 0.8285 (internal)/0.9831, 0.9779, 0.7948 (external), respectively (all P < 0.0001). Choroidalyzer's agreement with graders was comparable to the intergrader agreement across all metrics. Choroidalyzer is an open-source, end-to-end pipeline that accurately segments the choroid and reliably extracts thickness, area, and vascular index. Especially choroidal vessel segmentation is a difficult and subjective task, and fully automatic methods like Choroidalyzer could provide objectivity and standardization.