# An Empirical Evaluation of Nuclei Segmentation from H&E Images in a Real Application Scenario
## 5 Results
\begin{table}
\begin{tabular}{l l c c c c c c c c c} \hline \hline \multirow{3}{*}{**Method**} & \multirow{3}{*}{**Target**} & \multicolumn{8}{c}{**Source**} \\ \cline{3-10}  & & \multicolumn{4}{c}{**MIC17**} & \multicolumn{4}{c}{**BNS**} \\ \cline{3-10}  & & **F1** & **P** & **R** & **ODI** & **OHD** & **F1** & **P** & **R** & **ODI** & **OHD** \\ \hline \multirow{4}{*}{FCN} & GBM & 0.79 & 0.83 & 0.75 & 0.68 & 13.53 & 0.77 & 0.75 & 0.79 & 0.69 & 12.01 \\  & HNSC & 0.76 & 0.78 & 0.76 & 0.71 & 13.96 & 0.54 & 0.42 & 0.77 & 0.64 & 12.45 \\  & LCC & 0.70 & 0.64 & 0.77 & 0.70 & 13.32 & 0.49 & 0.39 & 0.66 & 0.60 & 11.89 \\  & LUSC & 0.71 & 0.78 & 0.66 & 0.67 & 21.59 & 0.51 & 0.41 & 0.68 & 0.58 & 15.23 \\ \hline \multirow{4}{*}{HED} & GBM & 0.81 & 0.87 & 0.76 & 0.72 & 12.80 & 0.82 & 0.84 & 0.79 & 0.72 & 12.64 \\  & HNSC & 0.80 & 0.87 & 0.75 & 0.70 & 18.23 & 0.71 & 0.66 & 0.79 & 0.71 & 15.89 \\  & LCC & 0.78 & 0.79 & 0.77 & 0.74 & 13.37 & 0.70 & 0.62 & 0.81 & 0.72 & 10.78 \\  & LUSC & 0.76 & 0.85 & 0.69 & 0.69 & 19.73 & 0.69 & 0.68 & 0.70 & 0.65 & 21.87 \\ \hline \multirow{4}{*}{Mask R-CNN} & GBM & 0.80 & 0.82 & 0.79 & 0.71 & 12.28 & 0.79 & 0.77 & 0.81 & 0.72 & 12.22 \\  & HNSC & 0.69 & 0.67 & 0.77 & 0.69 & 16.90 & 0.48 & 0.38 & 0.81 & 0.68 & 15.93 \\  & LCC & 0.59 & 0.48 & 0.77 & 0.73 & 14.17 & 0.42 & 0.29 & 0.76 & 0.67 & 12.43 \\  & LUSC & 0.69 & 0.69 & 0.69 & 19.16 & 0.57 & 0.49 & 0.70 & 0.66 & 19.64 \\ \hline \multirow{4}{*}{UNet} & GBM & 0.79 & 0.80 & 0.79 & 0.71 & 11.80 & 0.81 & 0.80 & 0.82 & 0.73 & 11.13 \\  & HNSC & 0.72 & 0.71 & 0.73 & 0.67 & 17.60 & 0.61 & 0.53 & 0.80 & 0.71 & 14.38 \\  & LCC & 0.71 & 0.65 & 0.79 & 0.74 & 13.20 & 0.57 & 0.43 & 0.84 & 0.71 & 10.88 \\  & LUSC & 0.69 & 0.70 & 0.68 & 0.67 & 21.20 & 0.64 & 0.58 & 0.73 & 0.68 & 18.40 \\ \hline \multirow{4}{*}{DIMAN} & GBM & 0.85 & 0.87 & 0.84 & 0.74 & 11.14 & 0.83 & 0.81 & 0.85 & 0.76 & 9.54 \\  & HNSC & 0.67 & 0.64 & 0.76 & 0.69 & 18.93 & 0.64 & 0.56 & 0.85 & 0.75 & 13.29 \\ \cline{1-1}  & LCC & 0.62 & 0.53 & 0.77 & 0.75 & 16.55 & 0.59 & 0.46 & 0.84 & 0.75 & 12.38 \\ \cline{1-1}  & LUSC & 0.71 & 0.72 & 0.71 & 0.71 & 21.03 & 0.62 & 0.55 & 0.73 & 0.68 & 21.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Generalisation ability of FCN, HED, UNet, Mask R-CNN and DIMAN nuclei segmentation methods trained on MIC17 and BNS datasets and tested using the single MIC17 subsets, which were GBM, LGG, HNSC and LUSC. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Data Subset** & **R** & **G** & **B** & **R Nuclei** & **B Nuclei** & **G Nuclei** \\ \hline MIC17 & 175 \(\pm\) 17.9 & 117 \(\pm\) 24.1 & 163 \(\pm\) 21.3 & 103 \(\pm\) 24.4 & 61 \(\pm\) 21.3 & 118 \(\pm\) 24.0 \\ BNS & 204 \(\pm\) 12.8 & 185 \(\pm\) 19.3 & 204 \(\pm\) 10.6 & 157 \(\pm\) 10.8 & 138 \(\pm\) 11.7 & 178 \(\pm\) 6.9 \\ GBM & 178 \(\pm\) 16.8 & 132 \(\pm\) 11.8 & 170 \(\pm\) 8.5 & 102 \(\pm\) 28.7 & 66 \(\pm\) 24.1 & 118 \(\pm\) 22.2 \\ HNSC & 177 \(\pm\) 13.3 & 119 \(\pm\) 13.5 & 164 \(\pm\) 8.2 & 117 \(\pm\) 26.0 & 72 \(\pm\) 21.8 & 125 \(\pm\) 11.1 \\ LCC & 173 \(\pm\) 26.6 & 102 \(\pm\) 26.9 & 149 \(\pm\) 19.6 & 89 \(\pm\) 11.5 & 43 \(\pm\) 6.6 & 97 \(As can be seen, both the models trained with the MIC17 and the BNS datasets underestimated the number of cells, but in different ways. Indeed, the models trained with the MIC17 dataset undersegmented the images by producing vast regions incorporating more cells and some background parts. On the contrary, the models trained with the BNS dataset produced smaller regions by segmenting only portions of cells and completely neglecting other cells. The leading cause of these results lies in the characteristics of the public datasets used for training, whose images, even if coming from different tissues, were all acquired at a magnification of 40\(\times\). Thus, the obtained models were not able to generalise to images acquired with different scale (magnification of 20\(\times\)), not even the HED and DIMAN methods, which are multi-scale by nature.