# VLSM-Net: A Fusion Architecture for CT Image Segmentation
## 4 Results and Discussion  
### Ablation Experiment  
To verify the validity of the model, we make comparisons between the V-Net model, the LSM and the VLSM-Net in the same publicly available datasets. The segmentation performance of different network structures is compared in the LiTS dataset, as shown in Table 1. In this dataset, it can be seen that the VLSM-Net model can achieve optimal segmentation results in the liver, in which the \(DSC\) is 92.73%, Sensitivity is 94.59% and Precision is 95.13%. Compared to the original LSM and the V-Net model, it shows that the segmentation results of the improved VLSM-Net model achieve improvements of 11.5% and 0.57% in \(DSC\), respectively. The reason for this is the LSM lacks a deep convolutional neural network, thus it cannot utilize the deep information of the image, and has limitations in image segmentation with blurred edges. On the other hand, the V-Net model is insufficiently accurate and smooth for image edge prediction. Figure 6 shows the predicted images of different segmentation models in the LiTS dataset from the front view. In Figure 6C, compared with the original input image and the label, the predicted result of the LSM include several disconnected parts, and its main area is smaller than that in the label (see Figure 6B). In Figure 6D, the predicted result of the V-Net model includes fewer disconnected parts, and in which the main area is similar with the label. The result of the VLSN-Net model is depicted in Figure 6E; there is only one area recognized, which is identical to the label, although a small part is missing. Figure 7 displays the same predicted images generated from the top view. By comparing with the original input image and label in Figure 7B, the predicted result of the LSM (see Figure 7C) includes only one part, but its segmentation boundary is quite rough. In Figure 7D, the predicted result of the V-Net model includes a smaller extra part, and the main area bears a closer resemblance to the label. In Figure 7E, the VLSN-Net model provides a more precise prediction of the main area without any additional parts. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(DSC\)**\%** & **Sensitivity/\%** & **Precision/\%** \\ \hline LSM & 81.23 & 83.24 & 85.24 \\ V-Net & 92.16 & 91.36 & 91.75 \\ VLSM-Net & 92.73 & 94.59 & 95.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the different algorithm models in LiTS17. Figure 6: From the front view, the predicted images of different segmentation models in LiTS dataset. (**A**) The original input image. (**B**) The label image. (**C**) The predicted image of the LSM. (**D**) The predicted image of the V-Net model. (**E**) The predicted image of the VLSM-Net. Compared with the predicted image of the VLSM-Net model, the predicted image of the LSM has more disconnected parts and its main area contains lots of extra parts. When compared with the traditional V-Net model, the predicted image of the VLSM-Net model is also more accurate, and closer to the real annotation. From the above results, the predicted image of the VLSM-Net model proposed in this paper is the most effective of the three methods. Compared with the VLSM-Net model's prediction, the predicted image of the LSM has more disconnected parts, and its main area contains lots of extra parts because the LSM is more sensitive to the problems of gray scale inhomogeneity and blurred edges in the original CT images. On the other hand, the VLSM-Net model is also more accurate and similar to the real annotation than the traditional V-Net model because of the use of the LSM, from which some boundary information is learned and used in the final prediction. Therefore, the VLSM-Net model can achieve better performance by combining the structural features of the V-Net model and the LSM in the LiTS dataset. Next, in order to demonstrate the validity and universality of the model, we make a further comparative experiment in a LUNA dataset.