## 3 Results  
We trained U-Net to segment each frames of otoscope videos. Using 10-fold cross validation, we noted Dice coefficient of 0.84 \(\pm\) 0.03. While investigating the effect of the segmentation process (as recommended by [PERSON] et al. [32]) was beyond the scope of this study, dice coefficient of greater than 0.70 generally indicates a good overlap. At test time for composite image generation, a frame is considered relevant if at least it contains an eardrum region of an experimentally determined threshold of 20%, of its size. TM-relevant frames were utilized to generate composite images (see the SelectStitch process in Figure 1). Table 2 gives the changes in scoring the composite images generated by SelectStitch compared to the base images among two rounds of assessments. An examination of Table 2 shows that when SelectStitch is used, the readers gave fewer responses of a score of 1 (Poor) and more responses of a score of 3 (Good) compared to the base composite images, regardless of round. Table 2 indicates that using SelectStitch composite images, ENT-I improved 32 images from category 1 (Poor) to 2 (Moderate), 18 images from 2 to 3 (Good), and 8 composite images from category 1 to 3; ENT-II improved 15 composite images from 1 to 2, 18 images from 2 to 3, and 24 composite images from category 1 to 3 and ENT-III improved 24 composite images from 1 to 2, 30 images from 2 to 3, and 17 composite images from category 1 to 3 for Round-1. To illustrate the effectiveness of our proposed framework for otoscope video stitching, we presented the composite images of three otoscope video clips in Figure 7.  
We used an ordinal logistic regression model to compare image scoring between SelectStitch and the base method and between two rounds. There is a significant difference in scoring between the base and proposed methods at \(p\) value of 0.0007, and the base method has 6.5 times higher risk of scoring less than the proposed method. The probabilities on scoring are listed in Table 3. At Round-1, the base method has probability of 0.43 for scoring below 2 and has probability of 0.84 for scoring below 3, but the proposed method has probability of 0.10 for scoring below 2 and has probability of 0.44 for scoring below 3. At Round-2, the base method has probability of 0.52 for scoring below 2 and has probability of 0.88 for scoring below 3, but the proposed method has probability of 0.14 for scoring below 2 and has probability of 0.53 for scoring below 3. In summary, the base method has higher probability for scoring less than the proposed method at both rounds. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Reader**} & \multicolumn{4}{c}{**Immprovements**} & \multicolumn{4}{c}{**Deterioration**} & \multicolumn{4}{c}{**Improvements**} & \multicolumn{4}{c}{**Deterioration**} \\ \cline{2-13}  & **1 \textgreater{} 3** & **1 \textgreater{} 2** & **2 \textgreater{} 3** & **3 \textgreater{} 1** & **2 \textgreater{} 1** & **3 \textgreater{} 2** & **1 \textgreater{} 3** & **1 \textgreater{} 2** & **2 \textgreater{} 3** & **3 \textgreater{} 1** & **2 \textgreater{} 1** & **3 \textgreater{} 2** & \\ \hline ENT-I & 8 & 32 & 18 & 0 & 0 & 6 & 36 & 13 & 22 & 21 & 0 & 0 & 4 & 40 \\ \hline ENT-II & 24 & 15 & 18 & 0 & 1 & 2 & 40 & 17 & 29 & 10 & 0 & 0 & 0 & 44 \\ \hline ENT-III & 17 & 24 & 30 & 0 & 1 & 2 & 26 & 13 & 40 & 11 & 0 & 0 & 2 & 34 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Changes in scores on the evaluation of SelectStitch composite images compared to base process composite images over two rounds for three experts where the symbol of \(>\)means “to”; the symbol of \(\leftrightarrow\) represents no change cases. Figure 7: Examples of successful composite results using our approach. The first row shows the images generated by the base processes and the second row shows the images by our approach, SelectStitch. Each column indicates the number of readers. Each reader gives the score of 1 for the base image and 3 for the proposed image.