# Extracting Retinal Anatomy and Pathological Structure Using Multiscale Segmentation
## 3 Experiments
### Comparison to other Segmentation Networks  
In this paper, a multiscale input CNN network is combined with preprocessed datasets to design a network with stronger learning ability and higher accuracy. The network is based on image multiscale patching, where 80% of the image patch is used for training and 20% of the image patch is used for verification. The training is done an epoch at a time, while the verification set is evaluated as a whole. The epoch lasts 100 iterations. The multiscale segmentation network is compared to the original PixelNet and Unet networks. The three networks are based on the same database. This paper compares the Se, Sp, and Acc indicators, and the results are reflected in the table of results. Our network results are shown in the Figure 9. Note that we are here to obtain the accurate segmentation effect of the various feature structures of the fundus image. This section compares the performance of our proposed method to related methods to demonstrate that the proposed multi-seg-CNN framework can indeed achieve better segmentation of retinal anatomy and pathological structure. Particularly, the proposed method is compared to three related state-of-the-art methods. The first is PixelNet ([PERSON] et al., 2017). The other is the classification baseline using the most widely accepted deep model, i.e., Unet ([PERSON] et al., 2015). The last  
Figure 9: We show the segmentation of fundus images after training (from our own database). (**a**) shows the segmentation of exudates, roth spots, blood vessels, and optic discs (from left to right). (**b**) shows the segmentation of retinal structures and lesions. method is v3+ ([PERSON] et al., 2018), which is Google's recent work on the segmentation. Our baseline model trains over three independent classification models using our own database for training. We then derive the segmentation method according to the clinical segmentation criteria. Figure 10 provides the ROC curve of each framework. All four methods use deep learning methods to train models using the same training data. The best segmentation threshold and the highest accuracy during testing is achieved for images from a single database. Images from this database also have the fewest number of false positives and false negatives. Our method has high segmentation accuracy. In second place is the PixelNet method, which also has high segmentation accuracy, but because it is a single branch, the accuracy is not as high as the four-branch method. The Unet segmentation effect is not ideal when blood vessels are separately segmented. The Deeplabv3+ segmentation method has the lowest accuracy, which leaves us stunned because DeepLabv3+ is generally effective for small targets where the overall structure is segmented. We will evaluate the Se, Sp, and Acc indicators for each feature structure, separately, for blood vessels, exudates, bleeding points, visual cup optic discs, and macula. Since the structures of the optic disc and the macula are relatively simple, and the features are obvious, this paper considers the two structures together. For the concentrated structure, the segmentation results of the other three network segmentation methods (for optic discs and the macula) are listed in Table 2.  
Comparing the three networks, we notice that the PixelNet accuracy is higher than that of Unet and our designed Mult-CNN; however, we also find that Multi-seg-CNN is better than Unet, which has similar accuracy to that of PixelNet. This is likely due to the fact that the optic disc and the macula are simple in structure, and the segmentation is relatively easy to identify, so the sensitivity and specificity of the three networks are not much different. We used similar methods to find the overlay structure and to obtain the blood vessel segmentation results in Table 3, the exudate segmentation results in Table 4, and the bleeding point segmentation results in Table 5.