# Learning to Segment Blob-like Objects by Image-Level Counting
## 4 Experiments and Results
### Results
#### 4.3.3 Undesirable Effects  
Unexpected shapes can occur in the segmentations extracted with the CAM-based method. Examples of shapes that occurred that way are shown in Figure 9. When training the same configuration multiple times, each new training may lead to different shapes marking the object for counting. The reason for that is a missing restriction since the counting \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multicolumn{3}{c}{**Only Spatial**} & \multicolumn{2}{c}{**With Tracking**} \\ \cline{2-5}  & **F1-Score** & **Recall** & **Precision** & **F1 Score** \\ \hline Dilated ResNet-18 & 0.871 & 0.936 & 0.839 & 0.941 \\ Dilated ResNet-34 & 0.870 & 0.893 & 0.873 & 0.914 \\ UNet++8 & 0.896 & 0.928 & 0.884 & 0.945 \\ UNet++16 & 0.890 & 0.894 & 0.909 & 0.941 \\ TransUNet & 0.873 & 0.947 & 0.828 & 0.929 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of a direct segmentation training on natural data followed by hard thresholding at 0.5 and connected components’ clustering. For the evaluation with TransUNet, the images had to be split into patches and reassembled after segmentation because this network only supports a constant image size. loss can be satisfied by different shapes as long as they follow a consistent pattern within one trained model. The problem with this effect is that some shapes are hard to detect in downstream blob detection, so additional losses are necessary to ensure suitable features. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multicolumn{2}{c}{**Only Spatial**} & \multicolumn{2}{c}{**With Tracking**} \\ \cline{2-5}  & **F1-Score** & **Recall** & **Precision** & **F1 Score** \\ \hline Dilated ResNet-18 & 0.879 & 0.925 & 0.854 & 0.937 \\ Dilated ResNet-34 & 0.844 & 0.830 & 0.894 & 0.922 \\ UNet++8 & 0.899 & 0.929 & 0.890 & 0.955 \\ UNet++16 & 0.897 & 0.942 & 0.871 & 0.952 \\ TransUNet & 0.817 & 0.946 & 0.744 & 0.868 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of training counting networks on natural images additionally employing the contrast loss (Section 3.3) and the morphological loss (Section 3.4). A following connected components’ clustering receives the output of the convolutional layer in the counting head after hard thresholding at 0.5. For the evaluation with TransUNet, the images had to be split into patches and reassembled after segmentation because this network only supports a constant image size. Figure 8: Example input (**a**), the mask of manually annotated objects (**b**), and segmentations created using the loss-based approach with different network architectures (**c**–**e**). The output determined on the basis of TransUNet had to be divided into patches due to the specification of a constant input size in combination with different image sizes in the test data. Because of this, abrupt changes can be seen at the borders of these sections due to object areas that have been split. A noticeable feature in the results is that two individual objects are combined in one area in the annotation mask (second marker from the right). Only the dilated ResNet (DRN34) separated the two objects correctly. In the case of natural SPR image data, additional effects occurred. The examples in Figure 10 show two segmentations that were generated using the Score-CAM approach after being trained on a counting task. In both images, relevant regions are marked in an undesired way in which the object segmentations are distributed over the surrounding area. This is due to the fact that the objects in SPR images, i.e., particle signals, are naturally surrounded by wave patterns when they are made visible by the surface plasmon resonance effect. The waves around a particle, which may be stronger or weaker depending on the experimental setup, particle sizes, and materials are, therefore, sometimes detected as a feature to discover a particle signal.