Optical coherence tomography (OCT) has gained momentum on segmenting anterior corneal substructures, such as treatment-induced flaps and lenticule cuts created by femtosecond lasers. However, recent semi-automated methods rely on manual markers, which can introduce bias and variability. In this work, we present an automated algorithm designed to overcome the limitations in the geometric quantification of intrastromal cuts produced by two different femtosecond lasers, using a unique imaging system. Our analysis, conducted on peri-operative segmentations of goat eyes, aims to demonstrate the method’s potential for contributing to ongoing efforts and enhancing clinical outcomes in refractive surgery treatment.

The Zernike representation of wavefronts interlinks low- and high-order aberrations, which may result in imprecise clinical estimates. Recently, the Gatinel–Malet wavefront representation has been introduced to resolve this problem by deriving a new, unlinked basis originating from Zernike polynomials. This new basis preserves the classical low and high aberration subgroups’ structure, as well as the orthogonality within each subgroup, but not the orthogonality between low and high aberrations. This feature has led to conversions relying on separate wavefront reconstructions for each subgroup, which may increase the associated numerical errors. This study proposes a robust, minimised-error (lossless) analytical approach for conversion between the Zernike and Gatinel–Malet spaces. This method analytically reformulates the conversion as a nonhomogeneous system of linear equations and computationally solves it using matrix factorisation and decomposition techniques with high-level accuracy. This work fundamentally demonstrates the lossless expression of complex wavefronts in a format that is more clinically interpretable, with potential applications in various areas of ophthalmology, such as refractive surgery.

Refractive correction techniques, such as Lenticule Extraction and LASIK, are pivotal in corneal surgery. Precise morphological characterization is essential for identifying post-operative complications, which can be compromised by image noise and low contrast. This study introduces an automated image processing algorithm that integrates non-local denoising, the Sobel gradient method, and Bayesian optimization to accurately delineate lenticule volumes and flap surfaces. Validated on 60 ex vivo porcine eyes treated with the SCHWIND ATOS femtosecond laser, the algorithm demonstrated high accuracy compared to the manual gold standard while effectively reducing variability.