Choroidalyzer: An open-source, end-to-end pipeline for choroidal
  analysis in optical coherence tomography
Justin Engelmann Jamie Burke Charlene Hamid Megan Reid-Schachter Dan Pugh Neeraj Dhaun Diana Moukaddem Lyle Gray Niall Strang Paul McGraw Amos Storkey Paul J. Steptoe Stuart King Tom MacGillivray Miguel O. Bernabeu Ian J. C. MacCormick
Purpose: 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.
  Methods: We used 5,600 OCT B-scans (233 subjects, 6 systemic disease cohorts,
3 device types, 2 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-centred region
of interest. We analysed 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.
  Results: 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 inter-grader agreement across all metrics.
  Conclusions: 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 standardisation.