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Automatic biometry of the anterior segment during accommodation imaged by optical coherence tomography
Dexi Zhu1, Yilei Shao, Lin Leng
1School of Optometry and Ophthalmology (D.Z., Y.S., L.L., Z.X., F.L., M.S.), Wenzhou Medical College, Wenzhou, Zhejiang, China; and Bascom Palmer Eye Institute (J.W.), Miller School of Medicine, University of Miami, Miami, FL.
A new software algorithm accurately measures human eye anterior segment dimensions using optical coherence tomography (OCT) imaging. This automated biometry demonstrates high precision and repeatability, promising for real-time accommodation studies.
Area of Science:
- Ophthalmology
- Biomedical Engineering
- Medical Imaging
Background:
- Accurate biometry of the human anterior segment is crucial for diagnosing and monitoring various ocular conditions.
- Traditional manual measurements can be time-consuming and prone to inter-observer variability.
- Advancements in optical coherence tomography (OCT) provide high-resolution imaging of ocular structures.
Purpose of the Study:
- To evaluate the accuracy and repeatability of a novel software algorithm for automated biometry of the anterior segment.
- To assess the performance of the algorithm when applied to images acquired with long scan depth OCT.
- To compare automated measurements with manual measurements for validation.
Main Methods:
- Custom-built long scan depth OCT was used for ocular anterior segment imaging.
- An automated software algorithm incorporating boundary segmentation, image registration, and optical correction was developed.
- Boundary segmentation utilized gradient information and dynamic programming for shortest path searching to optimize edge detection.
- Validation involved comparing automatic and manual measurements and conducting repeatability studies.
Main Results:
- The algorithm successfully obtained key anterior segment dimensions: central corneal thickness, anterior chamber depth, pupil diameter, crystalline lens thickness, and lens radii of curvature.
- No significant differences were found between automatic and manual measurements across all parameters.
- Intraclass correlation coefficients (ICC) for agreement between automatic and manual measurements ranged from 0.85 to 0.98.
- Coefficients of repeatability and ICC for automated dimensions were satisfactory, ranging from 1.1%-6.1% and 0.663-0.990, respectively.
Conclusions:
- The developed software algorithm demonstrates high accuracy, good repeatability, and fast execution for automated anterior segment biometry from OCT images.
- This automated approach shows significant promise for the real-time investigation of dynamic changes in the human anterior segment during accommodation.
