# ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images
## 6 Experimental Results
### Combination Protocols  
In this section, we present the results from combining SAM-b and the trained YOLOv8-m using the four protocols presented in Section 4.3. Table 4 presents the results found for each protocol. In this one, we consider the baseline and upper limit metrics as the random point prompts and the bound box (oracle) results using SAM-b, respectively (presented in Table 2). From these results, it is possible to notice that the greatest improvements were obtained by fusion Protocol #2 (prompt refinement), which uses point prompts to select a candidate and uses the candidate's bounding box as input to the SAM-b's prompt encoder. This protocol, achieved the highest average mDICE when compared to the other protocols, being equivalent to 0.7877. The greatest increase in metrics comes from the WT subjects. Such increments were equivalent to 0.2035 (\(\uparrow\) 37.85%), 0.2446 (\(\uparrow\) 48.83%) and 0.3016 (\(\uparrow\) 62.04%), respectively, for the 22 WT, 23 WT and 28 TW animal tags. The results show that we can improve our segmentation metrics by using prompt fusion enhancement, i.e., converting point prompts to bound box prompts. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Fusion Protocol**} & \multicolumn{6}{c}{**Animal Tag**} & \multirow{2}{*}{**Mean**} \\ \cline{2-2} \cline{4-7}  & **2C** & **5C** & **6C** & **22 WT** & **23 WT** & **28 WT** \\ \hline Baseline & 0.7321 & 0.7016 & 0.7704 & 0.5376 & 0.5009 & 0.4861 & 0.6215 \\ \hline
1 & 0.7296 & 0.7456 & 0.7382 & 0.6945 & 0.6813 & 0.6733 & 0.7104 \\
2 & **0.8156** & 0.8336 & 0.8433 & **0.7474** & **0.7411** & **0.7455** & **0.7877** \\
3 & 0.7928 & 0.8193 & 0.8259 & 0.7301 & 0.7208 & 0.7208 & 0.7683 \\
4 & 0.8152 & **0.8368** & **0.8458** & 0.7418 & 0.7323 & 0.7406 & 0.7854 \\ \hline Upper Limit & 0.8885 & 0.8872 & 0.8906 & 0.8677 & 0.8630 & 0.8629 & 0.8766 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: mDICE values for each animal tag, considering the 4 fusion protocols. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Animal Tag** & **\# of Cells** & **\# Predicted Cells** & **\# Matches** & **mIoU** & **mDICE** \\ \hline
2C & 1590 & 1612 (1.38\%) & 1440 (90.56\%) & 0.5389 & 0.6582 \\
4C & 1513 & 1915 (+26.56\%) & 1449 (95.77\%) & **0.5835** & **0.7142** \\
5C & 2211 & 2582 (+16.77\%) & 2112 (95.52\%) & 0.5666 & 0.6988 \\
22 WT & 1386 & 1378 (\(-0.57\%\)) & 1195 (86.21\%) & 0.5250 & 0.6354 \\
23 WT & 1520 & 1666 (+9.6\%) & 1328 (87.36\%) & 0.5183 & 0.6371 \\
28 WT & 1489 & 1695 (+13.83\%) & 1313 (88.18\%) & 0.5035 & 0.6226 \\ \hline Mean & 1618.16 & 1808 (+11.26\%) & 1472.83 (90.60\%) & 0.5393 & 0.6611 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Total quantity of detected cells, matches, and metrics found by the YOLOv8-m model, for each animal tag. From the results of Protocol #1 (candidate-first), we conclude that it underperformed when compared to other protocols. This protocol selects the instance segmentation cells (YOLOv8-m outputs) to compose the final mask, which are already known to have a lower IoU than the target cells. Although it reached a smaller mDICE when compared to the other protocols, it still outperformed the standalone SAM-b and YOLOv8 models by, respectively, 0.0888 (\(\uparrow\) 14.30%) and 0.0493 (\(\uparrow\) 07.45%). Such an increase may be also justified by the fact that such an approach includes the ENCs that were not matched during the candidate generation phase. Similar behavior was also found when applying Protocol #3 (segmentation fusion). As we fused the output masks of the point-prompted SAM-b and YOLOv8-m, it was possible to notice that the intersection rule increased the results obtained by the standalone models by 0.1467 (\(\uparrow\) 23.62%) and 0.1071 (\(\uparrow\) 16.21%). This increment in the evaluation metrics indicates that we benefit from a late mask fusion, leading to the thought that the generated segmentation masks are complementary.