# An Empirical Evaluation of Nuclei Segmentation from H&E Images in a Real Application Scenario
## 5 Results  
Table 1 show the results of the cross-dataset and same-dataset experiments on the above-mentioned datasets with the selected nuclei segmentation methods. For ease of comparison, for each target dataset, the same-dataset performance is highlighted in grey. As can be seen, the same-dataset performances of the various methods are quite aligned with each other, with a slight advantage for the HED method in both datasets and on almost all metrics. Obviously, these results represent ideal performances since both the training and test images come from the same dataset. The most interesting results instead came from the cross-dataset comparison, which simulated the behaviour of the segmentation methods in a real application. As expected, the performances decreased significantly when the target dataset was different from the source dataset. The most evident drop in performance was observed when the target dataset was BNS. In this case, the only method with satisfactory results was DIMAN, while when the target dataset was MIC17, the drop in performance was less evident and both DIMAN and HED performed well. In general, in cross-dataset experiments, the most remarkable drop occurred in the \(R\) value, when the target dataset was BNS, and in the \(P\) value, when the target dataset was MIC17. In other words, it means that in the first case, the models tended to underestimate the number of cell nuclei (many false negatives), while in the second case they tended to overestimate the number of cell nuclei (many false positives). \begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Target**} & \multicolumn{8}{c}{**Source**} \\ \cline{3-11}  & & \multicolumn{4}{c}{**MIC17**} & \multicolumn{4}{c}{**BNS**} \\ \cline{3-11}  & & **F1** & **P** & **R** & **ODI** & **OHD** & **F1** & **P** & **R** & **ODI** & **OHD** \\ \hline \multirow{2}{*}{FCN} & MIC17 & 0.74 & 0.76 & 0.73 & 0.69 & 15.60 & 0.58 & 0.49 & 0.73 & 0.63 & **12.90** \\  & BNS & 0.52 & 0.60 & 0.46 & 0.54 & 23.16 & 0.73 & 0.73 & 0.74 & 0.69 & 14.75 \\ \hline \multirow{2}{*}{HED} & MIC17 & 0.79 & 0.85 & 0.74 & 0.71 & 16.03 & **0.73** & **0.70** & 0.77 & 0.70 & 15.30 \\  & BNS & 0.52 & **0.70** & 0.42 & 0.55 & 26.40 & 0.77 & 0.83 & 0.73 & 0.71 & 17.13 \\ \hline \multirow{2}{*}{UNet} & MIC17 & 0.73 & 0.72 & 0.75 & 0.70 & 15.95 & 0.66 & 0.58 & 0.80 & 0.71 & 13.70 \\  & BNS & 0.48 & 0.59 & 0.41 & 0.52 & 28.32 & 0.70 & 0.73 & 0.71 & 0.69 & 18.84 \\ \hline \multirow{2}{*}{Mask R-CNN} & MIC17 & 0.69 & 0.66 & 0.75 & 0.71 & 15.63 & 0.57 & 0.48 & 0.77 & 0.68 & 15.06 \\  & BNS & 0.53 & 0.57 & 0.50 & 0.56 & 21.52 & 0.68 & 0.66 & 0.72 & 0.68 & 18.96 \\ \hline \multirow{2}{*}{DIMAN} & MIC17 & 0.71 & 0.69 & 0.77 & 0.72 & 16.91 & 0.67 & 0.60 & **0.82** & **0.74** & 14.30 \\  & BNS & **0.61** & 0.66 & **0.60** & **0.62** & **19.56** & 0.73 & 0.73 & 0.76 & 0.72 & 16.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Same-dataset (highlighted in grey) and cross-dataset performances of FCN, HED, UNet, Mask R-CNN and DIMAN nuclei segmentation methods. The best cross-dataset result for each target (test) dataset is reported in bold. Both errors could have a significant influence in a real application that relies on the cellularity value to identify or grade the presence of pathology. To test the significance of our results, we used the Wilcoxon test [62], which is the statistical test recommended for this scenario [63]. This test demonstrated that the drop in performances between same-dataset and cross-dataset is statistically significant (\(p\)-value \(<\) 0.05) in almost all the used metrics except the OHD. To better highlight the statistical differences between the same-dataset and cross-dataset performances, we reported in Figure 3 the bar charts of the \(p\)-value obtained with the Wilcoxon test. We evaluated the null hypothesis for precision and recall obtained with the same-dataset and cross-dataset for all the nuclei segmentation methods trained with the MIC17 dataset (in red) and the BNS dataset (in blue).