# Semantic Image Segmentation Using Scant Pixel Annotations
## 5 Experimental Results and Discussion
### Biofilm Dataset  
For this dataset, experiments were conducted using \(J=8\) (10% of the training data) in each iteration. Three test images were randomly chosen for evaluation from the dataset, and rest of the data were used to generate three successive models using the SSPA approach. Models obtained using HE pixel label replacements outperformed others.  
**Pixel label replacements for HE images.** Figure 8 shows the results obtained using HE pixel label replacements. As shown in the figure, model \(M_{2}\) was obtained by replacing only 0.85% of the pixel labels from the 8 HE images in the output of model \(M_{1}\). Model \(M_{3}\) was obtained by replacing 3.73% of the pixel labels from the 8 HE images in the output of model \(M_{2}\). The performance of \(M_{1}\) and \(M_{2}\) was similar with IoU values around 0.691 and Dice scores around 0.815. The mean entropy of \(M_{2}\) decreased to 1.778 from 2.587 in \(M_{1}\). The mean entropy of \(M_{3}\) increased to 1.991, and the algorithm terminated. The benchmark model \(M_{0}\) had mean entropy 2.822, IoU 0.609, and Dice score 0.754.  
Figure 6: Performance on the melanoma dataset using the SSPA approach with LE and HE pixel label replacements.  
Figure 7: (**A**) Heatmap of confidence entropy values, (**B**) view of pseudo label before replacement with pixels (in red) to be annotated by expert, (**C**) enlarged view of (**B**).  
The heatmap in Figure 9A shows that the target regions for pixel label replacements of \(M_{2}\) were found within the bacterial cells, contrary to the melanoma datasets where the boundaries of objects showed the highest uncertainty. The uncertainty of the model within the bacterial cells is likely due to the unique nature of having to segment the biofilm dataset. Similar to the EM dataset, the goal of segmenting the biofilm dataset was to determine the boundary map of the bacterial cells. Figure 9B shows a more explicit view of the pixels (in red) to be annotated by the experts.  
Figure 10 illustrates the entropy distribution of HE pixel label replacement models with the y-axis representing the distribution of entropy values for each model in the x-axis. All models display a normal distribution with a few outliers. The best model, \(M_{2}\) had the lowest median, lower than the lower quartile of \(M_{0}\) and \(M_{1}\). The entropy distribution of both \(M_{2}\) and \(M_{3}\) showed decreasing variability, which illustrates the positive effect of pixel level replacements on model variability. We also observed that \(M_{3}\) had a higher rate of oscillation than \(M_{2}\), further validating the correlation between improved performance and lower oscillation.  
Figure 8: Performance on the biofilm dataset using the SSPA approach with HE pixel label replacements.  
Figure 9: (**A**) Heatmap of confidence entropy values, (**B**) view of the pseudo label before replacement with pixels (in red) to be annotated by expert.  
**Pixel label replacements for LE images.** On the other hand, model \(M_{2}\) with LE pixel label replacements (also depicted in Figure 8) recorded a mean entropy 1.802, IoU 0.469 and Dice score 0.634. \(M_{2}\) was generated using 0.33% pixel label replacements. The pixel replacements required by \(M_{3}\) was 0.35%. Since the mean entropy of \(M_{3}\), 2.541 was higher than that of \(M_{2}\), the algorithm terminated here.