# Semantic Image Segmentation Using Scant Pixel Annotations
## 5 Experimental Results and Discussion
### EM Dataset  
For this dataset, experiments were conducted using \(J=3\) (10% of the training data) in each iteration. Models obtained using LE pixel label replacements outperformed others. **Pixel label replacements in HE images. ** Our results in Figure 3 indicate that the performance of models with HE pixel label replacements did not improve, despite increased expert annotation efforts. In Figure 3, for each model and \(J\) value combination on the x-axis, three types of information are depicted--the IoU (black bar), Dice scores (blue bar), and the percentage of pixels replaced (the red trend-line). Models \(M_{2}\) and \(M_{3}\) had the same Dice (0.874) and IoU (0.776) scores. Model \(M_{2}\) was obtained by replacing 1.18% of the pixel labels from the 3 images in the output of model \(M_{1}\). Model \(M_{3}\) was obtained by replacing 2.46% of the pixel labels from the 3 images in the output of model \(M_{2}\). Since the mean entropy of \(M_{3}\) (2.976) was higher than that of \(M_{2}\) (2.845), the algorithm terminated. The models obtained using HE pixel label replacements achieved similar IoU values but had lower Dice scores in comparison to the benchmark model \(M_{0}\), which had mean entropy of 1.986, IoU of 0.823, and Dice score of 0.903. **Pixel label replacements in LE images. ** Figure 4 shows the results obtained using LE pixel label replacements. For this experiment, we randomly chose one image as a test image and trained \(M_{1}\) using the remaining 29 images. Next, we generated models \(M_{2}\) and \(M_{3}\) using \(J=3\). Model \(M_{2}\) was obtained by replacing 0.86% of the pixel labels from the 3 LE images in the output of model \(M_{1}\). Model \(M_{3}\) was obtained by replacing 1.69% of the pixel labels from the 3 LE images in the output of model \(M_{2}\). Since the mean entropy of \(M_{3}\) (2.447) was higher than that of \(M_{2}\) (2.441), the algorithm terminated. Model \(M_{2}\) with IoU value 0.818 and Dice score 0.9 performs comparably with \(M_{0}\), having mean entropy 1.953, IoU 0.82, and Dice score of 0.9. The \(M_{0}\) values for LE slightly differ from those for HE since they are computed using 29 instead of 30 images. We also studied the entropy distribution of models generated using the LE pixel label replacement strategy. The entropy distribution of \(M_{0}\) had high variability, while \(M_{2}\) had the least variability and the best performance. We studied the model prediction entropy distribution of models generated using LE pixel label replacement strategy. The results are displayed in Figure 5. Here, the x-axis plots the model and the y-axis plots the entropy values. Each box plot in the figure shows the entropy value distribution of images for each model. As can be seen from the figure, the entropy distribution of \(M_{0}\) had high variability, while \(M_{2}\) had the least variability and the best performance. Although the median entropy values across models \(M_{1}\), \(M_{2}\), and \(M_{3}\) were higher than the upper quartile of \(M_{0}\), the SSPA approach seemed to have reduced the variability in the entropy values to obtain better performance. Further, we studied pixel oscillation to understand the effectiveness of the pixel replacement using the SSPA approach. Figure 4: Performance on the EM dataset using the SSPA approach with LE pixel label replacements. Figure 3: Performance on the EM dataset using the SSPA approach with HE pixel label replacements. We define oscillating pixels as the pixels with normalized prediction confidence values in the target range [0.45,0.55] (\(\delta=0.05\)), which result in inverse prediction confidence values after being replaced by \(GT\) labels in the model input. Oscillating pixels can be problematic since they represent the unlearning of useful patterns in the input. We observed that 50.32% of the 0.86% replaced pixels in \(M_{2}\) oscillated, whereas 58.07% of the 1.69% replaced pixels in \(M_{3}\) oscillated.