# A Multi-Organ Segmentation Network Based on Densely Connected RL-Unet
## 3 Experiments
### Experiment Results
This is primarily due to the small proportion of pancreas representation in the dataset, which leads to interference from surrounding organs and tissues during feature extraction. Consequently, a considerable amount of edge information is lost, compromising the model's overall segmentation accuracy for the pancreas. Figure 7a describes the average DSC scores obtained by different models with different training iterations on the Synapse dataset. As the number of training iterations increases, the average DSC score of our model is higher than that of other models. The DSC index, which calculates the similarity between the two samples, shows that our training results and labels are more similar, verifying the validity of our proposed model. Figure 6: Visualization of segmentation results on SYNAPSE dataset. (**a**) Ground Truth; (**b**) RL-Unet; (**c**) Attention Unet; (**d**) U-Net; (**e**) Swin-Unet. Figure 7: The segmentation results of U-Net, Swin-Unet, R2U-Net, Attention Unet, and RL-Unet algorithms visually. (**a**) Average DSC scores obtained by training different models. (**b**) Average HD obtained by training different models. (**c**) Comparison of loss curves of different algorithms. Figure 7b depicts the average HD resulting from different iterations of training using different models on the Synapse dataset. As can be seen from Figure 7b, the HD distance of our proposed model is superior to other models, significantly smaller than other models, and higher than the Att-Unet model only at 20 K, which may cause anomalies caused by isolated points, demonstrating that the result of dataset segmentation is good and close to the marked result. As can be seen from Table 2, the RL-Unet algorithm proposed in this paper can still achieve a good segmentation effect on the BraTS2021 dataset, with DSC indexes reaching more than 80%, achieving similar or even surpassing results with U-Net, Attention Unet, Swin-Unet, and other classical medical image segmentation algorithms. It shows that the algorithm has good generalization ability and robustness. To verify the generalization of the model, we performed another set of experiments on MRI images of the ACDC dataset. Table 3 shows the results of average DSC comparison between our proposed method and previous advanced methods. It can be clearly seen that compared with previous models such as AttnUnet, SwinUnet, TransUnet, and MT-Unet, the proposed method has a higher average DSC score; compared with PVTCASCADE model, the average DSC score is slightly lower, but it is still quite capable. As shown in Table 4, RL-Unet outperforms most SORT architectures on the BUSI dataset, achieving a higher DSC score. Notably, the performance of RL-Unet is slightly below that of U-Net3+. We attribute U-Net3+'s superior performance to its full-scale skip connections, which are particularly effective for accurately segmenting complex structures in medical images. To address the limitations of full-scale skip connections, we have introduced JCA and JSA into our architecture, enhancing its performance to a comparable level. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Methods** & **ET** & **TC** & **WT** & **DSC** \\ \hline U-Net [7] & 83.92 & 87.71 & 90.70 & 87.44 \\ U-Net++ [24] & 84.21 & 87.76 & 91.30 & 87.76 \\ U-Net3+ [9] & 82.21 & 86.52 & 91.17 & 86.63 \\ nnFormer [29] & 83.93 & 87.82 & 91.52 & 87.74 \\ AttU-Net [10] & 85.08 & 87.76 & 91.53 & 88.12 \\ Swin-Unet [20] & 84.16 & 86.77 & 91.01 & 87.31 \\ RL-Unet (ours) & 85.27 & 88.12 & 91.52 & 88.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Segmentation results of different methods on BRATS2021 dataset.