## 2 Materials and Methods
### Fully Automated SHS Detection and Quantification  
Figure 1 gives an overview of the algorithm. The VLE data before transform to circular display was collected for analysis (Figure 1B). It should be noted that the data here were already Fourier transformed and logarithmically compressed. From the VLE data, the tissue surface was identified using a 3D graph algorithm [20] (Figure 1C). An initial detection of candidate SHSs was performed using adaptive intensity thresholding and morphological operations by searching for low intensity \"voids\" (Figure 1D). The \"voids\" were selected based on their local intensity signature: i.e., they had lower intensity than surrounding regions, and included both SHSs and non-SHSs. Non-SHSs are mucus bubbles or other image artifacts that are non-biological. SHSs further consisted of \"gland-like\" SHSs and lymphatic ducts and vessels. The goal of this step was to detect all possible SHSs and therefore false positives were expected. Then, a machine learning algorithm based on a support vector machine (SVM) classifier [21] was used to classify SHSs vs non-SHSs (Figure 1E). The classifier was trained using a variety of features extracted from 1018 manually segmented SHSs in a training data set consisting of 109 images from 6 VLE pullbacks. 85 out of the 109 images were selected from BE regions, whereas the remaining 24 images were from squamous esophagus regions. The labeling and segmentation of SHSs in the training data were performed by an OCT image expert using a custom computer program. For the training data all possible \"SHSs\" were identified and segmented without attempting to differentiate lymphatic ducts or blood vessels from glands because a widely accepted, validated criterion was unavailable at this point. To build the SVM classifier, features were extracted from each SHS in cross-sectional VLE images, which included intensity, shape (eccentricity and solidity, see Section 2.3 and Figure 2), depth and size. Additional features such as the intensity and standard deviation of the surrounding region of each SHS were also added to help differentiate SHSs from surface mucous bubbles. These features effectively removed most of the false positives. The \begin{table}
\begin{tabular}{l l} \hline Age, mean \(\pm\) STD & 69.4 \(\pm\) 9.3 \\ \hline Male, n (\%) & 33 (94.3\%) \\ \hline Race, white, n (\%) & 34 (97.1\%) \\ \hline BE length, cm, mean \(\pm\) STD & 2.1 \(\pm\) 2.0 \\ \hline Treatment naïve patients, n (\%) 1 & 8 (22.9\%) \\ \hline Number of prior RFA sessions, n, mean \(\pm\) STD & 1.7 \(\pm\) 2.0 \\ \hline Highest pathology record of LGD, n (\%) 2 & 18 (51.4\%) \\ \hline Highest pathology record of HGD/IMC, n (\%) 2 & 14 (40\%) \\ \hline \end{tabular}
\end{table} 
Table 1: Patient demographics (n = 35). processing steps described above were performed in 2D cross-sectional images (B-scans) without using 3D volumetric information. Although it is difficult even for human experts to differentiate lymphatic ducts and vascular structures from \"gland-like\" SHSs based on cross-sectional images alone, lymphatic ducts have a much longer longitudinal length extent than \"gland-like\" SHSs. Therefore, candidate regions longer than a threshold of 0.5 mm (10 images) were automatically classified by the algorithm as lymphatic ducts. The threshold length was pre-determined experimentally with the training data. For simplicity, \"gland-like\" SHSs are simply referred as SHSs in subsequent text. Currently, the entire algorithm was implemented in MATLAB for rapid development, and was not optimized for high speed processing. We validated the SHS detection algorithm using a test dataset of 100 randomly selected images from 10 cases not in the training data. An OCT image expert manually segmented 896 SHSs from the test dataset. True positives (TP), false positives (FP) and false negatives (FN) of SHSs were calculated on a per-SHS basis by comparing the algorithm output to expert manual segmentation in individual cross-sectional images.