J. Eur. Opt. Society-Rapid Publ. 21, 10( 2025) 93
reproducible compared to automated methods. Our proposed approach aims to address these limitations through advanced image processing techniques.
Optical Coherence Tomography( OCT) is an essential imaging technology that has significantly assisted the field of medicine [ 21 – 24 ]. By providing non-invasive, highresolution cross-sectional images, OCT images enable medical professionals to accurately analyze internal structures.
OCT imaging enables high-resolution visualization of the eye’ s microstructures, establishing it as an indispensable tool for both diagnostic and treatment planning purposes [ 25 – 27 ]. Its ability to deliver real-time imaging with micrometer-level precision has elevated the standard of care in ophthalmology [ 28 – 32 ].
In refractive surgery, OCT imaging plays a pivotal role throughout the surgical process, from preoperative planning to postoperative assessment [ 2 ]. It provides critical data on corneal thickness, curvature, and epithelial profile. During surgery, OCT assists in verifying lenticule cuts or flap quality, while postoperatively, it aids in detecting and managing complications [ 33 ].
By providing high-resolution imaging of ocular structures, the use of OCT enables clinicians to make more informed decisions, which can contribute to improved surgical outcomes [ 34, 35 ]. However, challenges such as the presence of noise have been addressed through various advancements in imaging techniques, leading to enhanced image quality and more accurate assessments [ 36 – 39 ].
Speckle noise along with the scattering and small contrast through intrastromal OCT images complicates accurate segmentation( characterization) of peri-operative cuts [ 40 – 42 ].
Currently, manual measurements serve as the reference, but they can potentially introduce systematic inaccuracies. These arise from user precision and arbitrary marker positioning relative to intrastromal surfaces, leading to significant variations in similar measurements.
We propose a combined approach that involves denoising images and optimization to accurately determine the desired segments. The Sobel gradient is employed to enhance features in the captured images, which aids in localizing the intrastromal cuts. Bayesian optimization is then used to fine-tune the hyper-parameters of the segmentation algorithm.
A pioneering work of Li et al. provides a framework for corneal interference segmentation to detect flap interfaces and the other anatomical corneal layers [ 43 ]. The proposed algorithm utilizes a transverse average filter followed by an intensity scan to identify local maxima. While average filtering demonstrates reliable performance, it can blur edges and fine details. To address this, we applied Non- Local means( NL Means) denoising, which effectively preserves details while reducing noise. Advances in computational image processing have made NL Means approaches the preferred method for denoising and maintaining fine structures. However, the choice of denoising strategy depends heavily on image registration and the technology applied.
Additionally, an active contour model( commonly referred to as a“ snake”) was used to optimize boundary detection [ 44 – 46 ]. This model incorporates image gradients and a smoothness constraint to guide the contour towards sharp boundaries.
In this study, we initially applied a point-density scanning routine, which identifies the boundaries of flap or anterior lenticule cuts. Furthermore, a classifier and Bayesian optimization were employed to distinguish the boundaries of lenticule cuts. Both the“ snake” model and Bayesian optimization minimize a cost function, with the strategy varying based on the specific problem. However, unlike the“ snake” model, the proposed optimization does not require progressive filtering. On the other hand, our utilized optimization routine constantly assumes a smooth and non-singular underlying function.
As part of our deterministic study, we analyzed lasertreated, peri-operative cuts on porcine eyes to demonstrate the effectiveness of our method. Our work not only targets peri-operative characterizations, but also can be of use for postoperative residual monitoring.
This study is conducted in a laboratory setting, with a primary focus on validating the geometry of laser-generated corneal cuts prior to any surgical manipulation. By isolating laser-induced geometries from surgical effects, the performance of laser systems in a more controlled and precise manner can be evaluated.
We compared the results of our proposed method against manual measurements of identical substructures to validate its accuracy and reliability. In addition to matching the central measurements obtained by traditional approaches, our method demonstrated a significant advantage by enabling the characterization of key geometric properties, including lenticule power, transition zones, and incision angles. These characteristics could not be determined using previously developed methods that focused solely on central substructure measurements.
Furthermore, despite the ex vivo nature of the experiment, we observed a strong correlation between the intended design parameters and the automated measurements produced by our method. Such capabilities not only extend the scope of preoperative and peri-operative analysis but also pave the way for more comprehensive postoperative evaluations, ultimately contributing to improved surgical predictability and patient outcomes.
Our approach can potentially fill a current gap in precise reassessment of intrastromal laser cuts where manual expert measurements were seen as the conventional( user-based) standard. Our method can realize the integration of advanced fit routines or machine learning approaches for comprehensive live characterization.
We note that the primary objective of this work is to demonstrate and validate the generalizability and robustness of our proposed approach, rather than to derive specific clinical conclusions.
2 Materials and methods
2.1 Experiment
OCT images were obtained with a Thorlabs GAN111 OCT consisting of a Ganymede GAN111 as the base unit and an OCTG9( with OCT-LK4-BB scan lens) as the scan head