Automatic fovea detection and choroid segmentation for choroidal thickness assessment in optical coherence tomography.

AIM: To develop an automated model for subfoveal choroidal thickness (SFCT) detection in optical coherence tomography (OCT) images, addressing manual fovea location and choroidal contour challenges.

METHODS

Two procedures were proposed: defining the fovea and segmenting the choroid. Fovea localization from B-scan OCT image sequence with three-dimensional reconstruction (LocBscan-3D) predicted fovea location using central foveal depression features, and fovea localization from two-dimensional en-face OCT (LocEN-2D) used a mask region-based convolutional neural network (Mask R-CNN) model for optic disc detection, and determined the fovea location based on optic disc relative position. Choroid segmentation also employed Mask R-CNN.

RESULTS

For 53 eyes in 28 healthy subjects, LocBscan-3D's mean difference between manual and predicted fovea locations was 170.0 µm, LocEN-2D yielded 675.9 µm. LocEN-2D performed better in non-high myopia group (P=0.02). SFCT measurements from Mask R-CNN aligned with manual values.

CONCLUSION

Our models accurately predict SFCT in OCT images. LocBscan-3D excels in precise fovea localization even with high myopia. LocEN-2D shows high detection rates but lower accuracy especially in the high myopia group. Combining both models offers a robust SFCT assessment approach, promising efficiency and accuracy for large-scale studies and clinical use.