J. Eur. Opt. Society-Rapid Publ. 21, 10( 2025) 103
Figure 11. User based measurements of flap and lenticule thicknesses. Significant variation on lenticule based measurements were observed.
Figure 12.( a, b) Examples of instrastromal flap( incomplete) cut at 75 nJ laser pulse energies.
incorporation of more sophisticated modeling to resolve finer substructures.
One limitation of our approach is the reliance on conventional heuristics for setting parameters such as d and h. This legitimacy directly impacts the noise removal algorithm and, consequently, the performance of the Sobel gradient. The current method involves fine-tuning of these parameters using a small set of data. While this approach has not shown significant variation across the entire dataset, it limits the effectiveness of patch-specific fine-tuning. To streamline the image processing pipeline, a standardized capture routine could be beneficial. Computationally, more sophisticated techniques such as Grid and Elastic search could be explored for parameter tuning [ 72, 73 ]. These methods could potentially reduce the manual effort involved in parameter optimization and improve the overall robustness of the algorithm.
For all collected datasets of porcine eye the peak prominence range was identical. No case showed a fluctuating R 2 versus prominence or a monotonic behavior since the start. The BO also was of necessity for all cases, though the drop down of MSE might occur earlier than four iterations.
Three major parameters conventionally specify the quality of the flap and lenticule intrastromal cuts as the spot distance, track distance and the pulse energy( nJ). Certain combinations of these parameters would lead to improve intrastromal cuts. However, incomplete cuts are probable to observe( Fig. 12). Those intrastromal patterns( as Fig. 12a) lead to low values of R 2 score( of fit) that rejects proceed of segmentation. In contrary, although the flap in Figure 12b was not performed perfectly, the R 2 score still gains a value higher than 95 %. A shorter flap diameter compared to the planned geometry could qualify the performance of the treatment system on creating intrastromal cuts.
In our model, a refractive index of 1.37 was employed for vertical measurements, which is a commonly accepted approximation. Nevertheless, it is important to acknowledge that the accuracy of these measurements is influenced by the refractive index, which can vary substantially with the state of the corneal hydration. Since these are ex vivo measurements where tissue hydration is not strictly controlled, a more precise approach could involve performing the lenticule extraction( or flap cut) followed by direct measurement with a micro-meter. This consideration will be addressed in future studies to further enhance accuracy.
Through analysis, the error propagation was generally low across all the parameters which may indicate that the uncertainties introduced by individual measurements had a minimal impact on the final results. Specifically, for parameters such as the flap thickness, the low propagation of error suggests that the precision of the measurement system is well-maintained throughout the process.
For instance, when calculating the axial thickness, we found that the uncertainty from the resolution of boundary measurements propagated with only a small increase in overall uncertainty, as follows.
Considering Table 3, the FT measurement determines a r value of 1.93 which results in approximately ± 3.6 lm uncertainty for axial thicknesses.
The error propagation of uncertainties, when adding independent measurements, is given as follows,
r results ¼ X r 2 i: ð10Þ
Here, for two boundaries with equal resolution uncertainty( r r), the axial thickness uncertainty( r t) becomes, p r t ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi r 2 r þ p r2 r
¼ ffiffi 2 rr: ð11Þ