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Static compression optical coherence elastography for the measurement of porcine corneal mechanical properties ex-vivo

Quince Zachery, Alonso-Caneiro David, Read Scott A., Harkin Damien G., Collins Michael J.

Biocybernetics and Biomedical Engineering

2024

Vol. 44, no. 3

609--616

Significance: The biomechanical properties of the cornea are important for vision and ocular health. Optical coherence elastography (OCE) has the potential to improve our capacity to measure these properties. Aim: This study tested a static compression OCE method utilising a commercially available optical coherence tomography (OCT) device, to estimate the Young’s modulus of ex-vivo porcine corneal tissue. Approach: OCT was used to image corneal tissue samples before and during loading by static compression. The compressive force was measured with a piezoresistive force sensor, and tissue deformation was quantified through automated image analysis. Ten ex-vivo porcine corneas were assessed and the corneal thickness was also measured to assess the impact of corneal swelling. Results: An average (standard deviation) Young’s modulus of 0.271 (+/- 0.091) MPa was determined across the 10 corneas assessed. There was a mean decrease of 1.78 % in corneal thickness at the end of the compression series. These results showed that there was a moderate association between corneal thickness and the Young’s modulus recording (R2 = 0.274). Conclusions: Optical coherence elastography utilising clinical instrumentation, can reliably characterise the mechanical properties of the cornea. These results support the further investigation of the technique for in-vivo measurement of the mechanical properties of the human cornea.

Segmentation of anterior segment boundaries in swept source OCT images

Garcia Marin Yoel, Skrok Marta, Siedlecki Damian, Vincent Stephen J., Collins Michael J., Alonso-Caneiro David

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 3

903--915

Quantification of the eye’s anterior segment morphology from optical coherence tomography (OCT) images is crucial for research and clinical decision-making, including the diagnosis and monitoring of many ocular diseases. Structural parameters, such as tissue thickness and area are the most common metrics used to quantify these medical images, and tissue segmentation is required before these metrics can be extracted. Currently, swept source OCTallows the capture of cross-sectional images that encompass the entire anterior segment with a high level of detail. However, the manual annotation of tissue boundaries is time-consuming. In this work, an algorithm based on graph-search theory combined with boundary-specific image transformation is applied for the segmentation of anterior segment OCT images. We demonstrate that the method can reliably segment 5 different tissue boundaries in healthy eyes with low boundary error (mean error below 1 pixel across all boundaries). The technique can be used to extract clinically relevant parameters such as central corneal and crystalline lens thickness as well as anterior chamber depth and area, with a high level of agreement with manual segmentation (normalized errors below 1.6%). The proposed method provides a tool that can support clinical and research OCT data analysis.