## 3 Comparative Experiment
### Comparative Experiment
#### 3.3.3 Additional Experiment Based on EMDS-6 Dataset  
To demonstrate the applicability of the models in our comparative experiments, we compared five models in pixel-level experiments on the EMDS-6 dataset. A partial EM sample of EMDS-6 is shown in Figure 10. EMDS-6 contains 840 EM's microscopic images within 21 classes. We divided the dataset into training, validation, and test sets in a 1:1:2 ratio. We trained five models using the same parameters as those used in the EMDS-5 experiments. The performance metrics of the experimental results of the five models are  
Figure 8: Reconstruction of pixel-level segmentation results on transparent images of the test set. (The figure contains the original image, ground truth image and U-net, Trans-Unet, Swin-Unet, U-Net++, and SegNet network model predicted segmentation results).  
Figure 9: A comparison of the image segmentation results of the IOU curves of deep learning on pixel-level training and validation sets.  
presented in Table 7. We found that on EMDS-6, the pixel-level segmentation performance is consistent with the segmentation performance of EMDS-5. The segmentation performance of the U-Net, U-Net++, and Swin-Unet models was similar, and the segmentation accuracy was approximately 95%. SegNet exhibited the worst segmentation performance, with an accuracy of 91.21%. In addition, the number of images in EMDS-6 was twice that of EMDS-5; therefore, the model learns more EM information during training, leading to an overall improvement in the segmentation performance of the five models. The loss and IOU curves trained using the five models are shown in Figures 11 and 12, respectively. We found that the loss and IOU curves trained by the five models were similar to the results in EMDS-5; therefore, the five models are suitable for comparison experiments. The segmentation results are shown in Figure 13.  
Figure 10: Examples of the environmental microorganism image in EMDS-6. (**a**) is the original images of EMDS-6, each image contains one or more EM objects of the same species, and one image is selected for each species as a representative. (**b**) correspond to the real segmentation images of microorganisms in each image in the (**a**). The pixel value of the background part in the microorganism image is set to 0, and the foreground part is set to 1.  
Figure 11: A comparison of the image segmentation results of the loss and accuracy curves of deep learning on pixel-level training and validation set of EMDS-6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dice** & **Jaccard** & **Precision** & **Recall** & **Acc** \\ \hline U-Net & 84.81 & 76.24 & 88.83 & 83.53 & 95.43 \\ U-Net++ & 86.48 & 78.25 & 89.02 & 87.08 & 95.80 \\ SegNet & 74.63 & 62.50 & 73.88 & 83.59 & 91.21 \\ Trans-Unet & 84.66 & 76.09 & 86.04 & 86.88 & 94.98 \\ Swin-UNet & 86.11 & 78.05 & 89.46 & 85.79 & 95.49 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Segmentation performance of models of five-fold cross-validation experiment on the EMDS-6 test set.  
Figure 12: A comparison of the image segmentation results of the IOU curves of deep learning on pixel-level training and validation sets of EMDS-6.