## 3 Results and Discussion
### Comparison of Models Performance  
Results of the Jaccard index for the different experiments are shown in Table 4. Tables with all metrics are provided in the Supplementary Material (Tables S2-S5). When reporting over the test set in CVC-EndoSceneStill, the best results are distributed between models trained with that same dataset and models trained with Kvasir-SEG. On the other hand, results in Kvasir-SEG and PICCOLO test sets are, in all cases but one, obtained when models are trained with the same dataset. Lastly, if all test sets are gathered and analysed together as whole, the best performance is obtained with models trained using the PICCOLO dataset. Models trained with Kvasir-SEG show better performance than those trained with CVC-EndoSceneStill. Therefore, it can be observed that the PICCOLO dataset allows for training more generalized models, that perform more uniformly along all datasets, rather than obtaining the best results for its own test set. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Dataset for**} & \multicolumn{4}{c}{**Dataset for Test Set**} \\ \cline{3-6}
**Training/Validation Set** & **Network** & \multicolumn{3}{c}{**CVC-EndoSceneStill**} & **Kvasir-SEG** & **PICCOLO** & **All** \\ \hline \multirow{6}{*}{CVC-EndoSceneStill} & U-Net + VGG16 & 56.80 \(\pm\) 36.09 & 46.29 \(\pm\) 29.36 & 32.82 \(\pm\) 34.81 & 42.69 \(\pm\) 35.13 \\  & U-Net + Densent121 & 67.31 \(\pm\) 34.47 & 56.12 \(\pm\) 34.29 & 38.76 \(\pm\) 39.05 & 50.88 \(\pm\) 38.51 \\  & LinkNet + VGG16 & 62.77 \(\pm\) 33.81 & 51.13 \(\pm\) 27.67 & 30.55 \(\pm\) 33.57 & 44.51 \(\pm\) 34.86 \\  & LinkNet + DenseNet121 & **72.16 \(\pm\) 39.03** & **56.69 \(\pm\) 33.68** & **39.52 \(\pm\) 37.90** & **52.63 \(\pm\) 37.53** \\  & U-Net + VGG16 & 32.44 \(\pm\) 38.79 & 64.23 \(\pm\) 29.78 & 28.51 \(\pm\) 35.12 & 39.50 \(\pm\) 37.98 \\  & U-Net + DenseNet121 & **71.82 \(\pm\) 29.87** & 74.13 \(\pm\) 23.40 & 44.78 \(\pm\) 38.73 & **59.87 \(\pm\) 35.72** \\  & LinkNet + VGG16 & 58.62 \(\pm\) 36.18 & 72.53 \(\pm\) 23.92 & 40.43 \(\pm\) 36.41 & 54.04 \(\pm\) 35.99 \\  & LinkNet + DenseNet121 & 69.10 \(\pm\) 32.53 & **74.52 \(\pm\) 22.81** & **44.92 \(\pm\) 37.37** & 59.35 \(\pm\) 35.33 \\  & U-Net + VGG16 & 47.76 \(\pm\) 36.46 & 52.64 \(\pm\) 30.41 & 58.74 \(\pm\) 36.06 & 54.24 \(\pm\) 34.93 \\ PICCOLO & U-Net + DenseNet121 & 62.41 \(\pm\) 34.78 & **65.33 \(\pm\) 30.66** & **64.01 \(\pm\) 36.23** & **63.97 \(\pm\) 34.35** \\  & LinkNet + VGG16 & 54.58 \(\pm\) 35.63 & 58.59 \(\pm\) 34.00 & 54.46 \(\pm\) 38.88 & 55.76 \(\pm\) 35.87 \\  & LinkNet + DenseNet121 & **64.18 \(\pm\) 33.04** & 59.61 \(\pm\) 33.80 & 60.14 \(\pm\) 38.31 & 61.02 \(\pm\) 35.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Jaccard index for each experiment. Best value per train/validation set and test set is indicated in bold. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **CVC-EndoSceneStill** & **Kvasir-SEG** & **PICCOLO** \\ \hline Void area (\%) & 23.73 \(\pm\) 5.57 & 15.23 \(\pm\) 4.82 & 34.14 \(\pm\) 0.33 \\  & (27.83 \(-\) 14.62) & (28.44 \(-\) 6.16) & (34.33 \(-\) 33.20) \\ Polyp area relative to the valid area (\%) & 12.50 \(\pm\) 11.49 & 17.36 \(\pm\) 15.65 & 20.45 \(\pm\) 20.68 \\  & (66.15 \(-\) 0.75) & (83.66 \(-\) 0.61) & (86.11 \(-\) 0.00) \\ Mean value of brightness channel in & 0.560 \(\pm\) 0.006 & 0.622 \(\pm\) 0.003 & 0.532 \(\pm\) 0.078 \\ HSV & (1.000 \(-\) 0.000) & (1.000 \(-\) 0.000) & (1.000 \(-\) 0.000) \\ Histogram flatness measure & 0.858 \(\pm\) 0.121 & 0.419 \(\pm\) 0.443 & 0.855 \(\pm\) 0.215 \\  & (0.959 \(-\) 0.000) & (0.962 \(-\) 0.000) & (0.970 \(-\) 0.000) \\ Histogram spread & 0.252 \(\pm\) 0.088 & 0.218 \(\pm\) 0.070 & 0.214 \(\pm\) 0.086 \\  & (0.520 \(-\) 0.076) & (0.432 \(-\) 0.075) & (0.452 \(-\) 0.066) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characterization of the datasets. Results are reported as mean \(\pm\) standard deviation. Minimum and maximum values are indicated between brackets.