## 4 Discussion  
This study investigates the patch-level and pixel-level segmentation performance of five deep learning models on the EMDS-5 [4]. The comparison results based on the evaluation indicators are listed in Tables 4, 5, 6 and 8. In addition, to verify the generalization of the model, we used the same method to perform expansion experiments on the EMDS-6 dataset. The results are summarized in Table 4 and Figures 11-13. To improve the reliability of the conclusion, all experimental results in this study were repeated five times, and then averaged for the final result [50]. In the patch-level segmentation experiments, the performance indicators of the five models were compared [49]. We found that all five deep learning models were better at classifying transparent image backgrounds than foregrounds. We can speculate that the foreground features of the transparent image are similar to those of the background, and all  
Figure 14: Valid examples in EMDS-5 that fuse pixel-level segmentation and patch-level segmentation. From top to bottom, the images in each original image of EM, the EM represent GT image, pixel-level segmentation result, patch-level segmentation result, and combined result, respectively. (The red part in the figure is the segmentation result). models had a strong ability to classify the background. Further, we compare the CNN and ViT models among the five models [14]. We found that ResNet50 [25] model had the highest segmentation performance; however, the ViT [31] model was better than the CNN model in terms of training time and model convergence speed. The ViT network has evident advantages in the time of training the model, and the time consumption is much less than that in other models. We can speculate that the ViT model may further expand its advantages when trained using more training data. In the pixel-level segmentation experiment, we also conducted a comparative experiment between the CNN and ViT models. The model with the highest segmentation performance was U-Net++ [51]. Similarly, the Swin-UNet model [52], represented by the ViT model, had a better convergence speed than the CNN model. In addition, the accuracy of the Swin-UNet segmentation was better than that of U-Net, SegNet [53], and Trans-Unet [54]. The magnitude and index system of patch-level and pixel-level segmentation are different. Therefore, we cannot numerically compare the segmentation performance of the two groups of experiments. However, we made a full comparison between the patch-level and pixel-level visual segmentation results and fused the patch-level and pixel-level segmentation results to obtain a more complete segmentation result. In computer vision tasks, image segmentation for multi-size EMs [5,55] and for weakly visible EMs [56,57] are introduced in recent years, where multi-scale CNN and pair-wise CNN methods are developed. However, the transparent EM image has not been developed and studied, so this paper used patch-level and pixel-level methods to segment transparent images, and a total of eight CNN and two ViT models were used to test the performance of the model. This study provides an analysis table of differences between the patch-level and pixel-level models. Our research and conclusions significantly reduce the workload of the researcher's choice of experimental augmentation method. This reference is of great significance. In this paper, we investigate deep learning methods for analyzing transparent EM images. In Section 2.1, we mentioned some existing techniques for analyzing transparent objects, and these techniques also have great potential in analyzing transparent EM images. For example, cleargrasp uses a deep convolutional network to infer surface normals, masks and occlusion boundaries on the surface of transparent objects. These outputs are then used to optimize the initial depth estimates of all transparent surfaces in the scene [17]. The leargrasp algorithm has great potential for transparent EM edge segmentation.