## 4 Discussion  
This is the first report of a fully automated algorithm for SHS detection and quantification in OCT images acquired using the commercially available VLE instrument, without requiring any pre-selection of regions of interests or manual input. This is also the first study to apply automated algorithms to investigate the general distribution and characteristics of SHSs in [PERSON]'s esophagus and the association of SHSs with RFA treatment response. The algorithm can be readily applied to any existing VLE images to extract quantitative information from large amounts of data, which is impractical with manual analysis. With further speed optimization, it could also be applied for real-time analysis to provide clinical information during endoscopic procedures. The SHS quantification algorithm can also be used as an input for other computer-aided algorithms. For example, the features of SHS can be combined with other clinical features for dysplastic BE classification [18]. The conventional way to analyze SHSs is to assess their presence [9; 22] or to manually count their number [10], which is time-intensive, and cannot comprehensively characterize SHSs. The algorithm developed in this study not only automates the above tasks, but augments the capability to assess additional features such as the SHS intensity, depth and shape information, which may also be clinically important. As shown in this study, we found that the depth and solidity of SHSs in the BE region were more significant indicators of post-RFA treatment response than the number. It is likely that thicker BE is harder to eradicate completely [23], and conceivable that higher SHS irregularity is associated with more advanced stage of BE. These subsurface features cannot be visualized or analyzed using conventional white light endoscopy. [PERSON]'s glands have been described to evolve from columnar-lined mucosa without goblet cells, to glands showing intestinal gene expression and to specialized metaplastic glands [24]. More glandular distortions such as branched or cribriform glands may have more malignant potential. The proposed algorithm can be useful to track the changes of gland characteristics and identify the early markers for metaplasia and dysplasia. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Cumulative Metrics** & \multicolumn{2}{c}{**Age \(\leq\) 70 (n = 10)**} & \multicolumn{2}{c}{**Age \(>\) 70 (n = 11)**} & \multicolumn{2}{c}{**Age \(\leq\) 70 (n = 10)**} & \multicolumn{2}{c}{**Age \(>\) 70 (n = 11)**} \\ \hline R & \(p\)-value & R & \(p\)-value & R & \(p\)-value & R & \(p\)-value \\
2D number & \(-\)0.185 & 0.609 & 0.178 & 0.601 & - & - & - & - \\
3D number & \(-\)0.180 & 0.605 & 0.243 & 0.471 & - & - & - & - \\ Volume (mm\({}^{3}\)) & \(-\)0.020 & 0.956 & 0.103 & 0.763 & - & - & - & - \\ \hline
**Metrics on a per gland basis** & \multicolumn{4}{c}{**Mean of all SHSs**} & \multicolumn{4}{c}{**Mean of cross-sectional extreme**} \\ \hline Size (mm\({}^{2}\)) & \(-\)0.240 & 0.504 & \(-\)0.047 & 0.891 & \(-\)0.246 & 0.493 & 0.047 & 0.891 \\ Depth (mm) & 0.129 & 0.722 & 0.327 & 0.326 & 0.228 & 0.527 & 0.365 & 0.270 \\ Intensity & \(-\)0.326 & 0.358 & \(-\)0.065 & 0.848 & \(-\)0.062 & 0.866 & 0.122 & 0.722 \\ STD & 0.209 & 0.562 & \(-\)0.065 & 0.848 & 0.129 & 0.722 & 0.019 & 0.956 \\ Orientation (degrees) & 0.640 & 0.046 \({}^{*}\) & \(-\)0.365 & 0.270 & 0.825 & 0.003 \({}^{**}\) & \(-\)0.327 & 0.326 \\ Eccentricity (0–1) & \(-\)0.884 & 0.001 \({}^{**}\) & 0.355 & 0.284 & \(-\)0.850 & 0.002 \({}^{**}\) & 0.355 & 0.284 \\ Extent (0–1) & 0.695 & 0.026 \({}^{*}\) & 0.467 & 0.147 & 0.695 & 0.026 \({}^{*}\) & \(-\)0.514 & 0.106 \\ Perimeter (mm) & \(-\)0.714 & 0.720 & 0.280 & 0.403 & \(-\)0.480 & 0.160 & 0.234 & 0.489 \\ Solidity (0–1) & 0.585 & 0.075 & 0.570 & 0.067 & 0.517 & 0.126 & \(-\)0.567 & 0.069 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Correlation between pre-ablation squamous SHS metrics and the relative change of maximum BE length after RFA in different age groups.