102
J. Eur. Opt. Society-Rapid Publ. 21, 10( 2025)
Figure 10. Regression representation comparing intended lenticule-flap diameter and thickness with the intended values. Thicknesses are in lm and diameters in mm.
For all panels in Figure 10, the regression analysis shows R 2 > 0.96andp < 0.001 which indicates a strong statistical relationship between the estimated and intended values.
The intrastromal cuts were scanned across four meridians with a 45 ° angular step size( referred to as bunches). All scans from the bunches were processed by the algorithm for characterizations. No significant fluctuations in thickness or diameter were estimated within these bunches. For each quantity( such as FT, FD, LT, and others) within each bunch, a StdDev was calculated. In Table 3, wereportthe maximum StdDev observed for each quantity across all bunches. The highest variations in estimates, although still minor, were observed in bunches with larger diameters or thinner thicknesses. It can characterize the highest level of variability observed for each particular quantity among all the bunches. The repeatability of the overall characterization pipeline( estimated for the maximum within bunch StdDev values) results in a 95 % confidence interval of ± 200 lm for determining diameters.
Since the algorithm is deterministic, multiple runs of the algorithm on the same scan always produce an identical segmentation( Dice similarity of 1 [ 70 ]). In contrast, the manual measurements performed by independent( skilled) users often struggled to converge to a unique solution. This discrepancy is particularly evident in thickness measurements.
As shown in Figure 11, two skilled users performed manual measurements on a subset of data. The manual measurements revealed a higher deviation in LT compared to FT, due to the smaller thicknesses involved. Overall, comparing the average of linear regressions between Figures 10b and 10d and Figures 11a and 11b reflect the same underlying trend. Despite these challenges, the manual measurements
Table 3. Representative of variation across bunches of four meridional scans, used as a measure to illustrate induced system variation.
Category
FD FT CD LD CT LT
and algorithmic characterizations are correlating, with a positioning error of approximately 10 lm.
4 Discussion
Max StdDev throughout bunches
100 lm 1.93 lm 115 lm 85 lm 2.25 lm 1.32 lm
While Canny and Hildreth methods could potentially outperform the Sobel operator, they require significant tuning [ 71 ]. In particular, the Canny method, with additional adjustments, can segment both ends of a single cut. Nevertheless, for our initial approach, this level of precision was not critical. The Canny edge detection method, with its Non-Maxima suppression, produces smoother edges. However, the rougher edges detected by the Sobel operator were sufficient for the requirements of this particular application. Future developments may necessitate more sophisticated modeling to capture finer substructures. Further advancements may, nonetheless, necessitate the