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 (=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.