## 4 Results
### Neural Network Application Results  
For an accurate and reproducible comparison of our evolution strategy-based post-processing compared with the raw segmentation results, we first selected the best segmentation algorithm out of our 4 created models.  
As shown in Table 1, representing the achieved segmentation results using the validation dataset, it is the case that the conversion of RGB images to their corresponding luminance variant leads to improved segmentation quality for both types of neural networks. Comparing the segmentation quality of the used Mask R-CNN and Unet++, it is also the case that the Mask R-CNN outperforms the Unet++.  
Our decision to use Mask R-CNN based on luminance input data is also supported by the results achieved using the test dataset as shown in Table 2. Based on this model selection, we analyze the classification performance of our luminance-based models. As shown in Table 3, it is still the case that the Mask R-CNN trained on the luminance data provides the highest accuracy. In addition, it can also be seen that the use of our grid-based filter improves the classification accuracy, especially the accuracy of the monolayer class.  
Further analyzing the classification accuracy of the best performing model, Table 4 provides detailed classification measures for our Mask R-CNN trained on luminance-only images with and without the use of our grid-based filter.  
In particular, the significant improvement of the precision score of the monolayer class highlights the improved ability to detect this class correctly. The low precision and still high recall score of the raw, unfiltered result shows the model's incapability to correctly separate the monolayers from the similar-looking empty Petri dish bottom without our grid-filtering-based post-processing, showing the importance of the filtering process based on the grid.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Luminance Conversion** & **Dice** & **Jaccard** \\ \hline Mask R-CNN & yes & 0.8982 & 0.8154 \\ Unet++ & yes & 0.8910 & 0.8036 \\ Mask R-CNN & no & 0.8970 & 0.8134 \\ Unet++ & no & 0.8785 & 0.7838 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of binary segmentation results on the validation dataset between the Mask R-CNN and the Unet++.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Luminance Conversion** & **Dice** & **Jaccard** \\ \hline Mask R-CNN & yes & 0.9069 & 0.8298 \\ Unet++ & yes & 0.9039 & 0.8249 \\ Mask R-CNN & no & 0.9014 & 0.8206 \\ Unet++ & no & 0.8974 & 0.8141 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of binary segmentation results on the test dataset between the Mask R-CNN and the Unet++.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **ES Based** & **F1 Monolayer** & \
\begin{tabular}{c} **F1 Forming** \\ **Stage** \\ \end{tabular} &
\begin{tabular}{c} **F1 Final** \\ **Spheroid** \\ \end{tabular} \\ \hline Mask R-CNN & yes & 0.7309 & 0.8562 & 0.8829 \\ Unet++ & yes & 0.4 & 0.8135 & 0.8127 \\ Mask R-CNN & no & 0.2879 & 0.8187 & 0.8829 \\ Unet++ & no & 0.0986 & 0.6615 & 0.8127 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of classification and localization results on the test dataset between the Mask R-CNN and the Unet++ based on the luminance-only data compared with the non-filtered version.  
Even if the difficult-to-classify monolayer class is still not perfectly detected using our post-processing workflow, the example images of our test dataset shown in Figure 9 prove the significant quality improvement due to the corrected classification by our post-processing workflow.