Automatic fovea detection and choroid segmentation for choroidal thickness assessment in optical coherence tomography
Year: 2024
Type: article
Abstract: 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: =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.
Authors: Chen Yu Lin, Yi Kit Chan, Wei Ping Hsia, Yu Len Huang, Chia Jen Chang
Institutions: Taichung Veterans General Hospital, Tunghai University
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,800
OpenAlex ID: W4402614750