# A Multi-Organ Segmentation Network Based on Densely Connected RL-Unet
## 3 Experiments
### Experiment Results  
The parameters of DSC and HD obtained by the segmentation of eight abdominal organs by RL-Unet in the Synapse dataset are compared with the classical segmentation networks ViT, U-Net, Swin-Unet, Attention Unet (AttU-Net), GCASCADE, nnFormer, MISSFormer, and Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net). The experimental results are shown in Table 1.  
Compared with basic ViT and U-Net, DSC and HD parameters of RL-Unet have been greatly improved, which demonstrates the efficiency of the combination of CNN and Transformer. Secondly, compared with the classical medical segmentation algorithm U-Net, the parameters of DSC and HD of RL-Unet increased by 3.1% and 17.63 mm, respectively, indicating the feasibility of the segmentation effect of RL-Unet and its sensitivity to small organs. Compared with Swin-Unet, AttU-Net, and R2U-Net, the parameters of DSC and HD of RL-Unet are still greatly improved, which indicates that RL-Unet pays attention to local information and extracts and integrates multi-scale information in context, effectively improving the segmentation accuracy. Although our proposed algorithm exhibits a slightly lower DSC compared to the latest MISSFormer and nnFormer algorithms, we are unable to precisely reproduce the results of the original authors due to machine limitations and variations in data processing methods. To validate the effectiveness of our proposed algorithm, we conducted experiments on the BraTS2021, ACDC, and BUSI datasets. As shown in Figure 6, we can see that the boundary of RL-Unet segmentation is clearer and smoother than that of other algorithms. It can be seen from the second line that several algorithms are missing the segmentation of pancreas, and the segmentation boundary of stomach in this dimension is poor, which overlaps with the prediction of pancreas. It can be seen from the three lines that several algorithms have a good segmentation effect and high segmentation accuracy for large targets such as the liver and stomach. Other algorithms resulted missing segmentation and over-segmentation on the pancreas. However, our proposed algorithm RL-Unet has a significantly better segmentation effect on the pancreas and small target organs than other algorithms, basically completely segmenting the pancreas and stomach. The fourth line reveals that the Attention Unet and Swin-Unet algorithms have a poor segmentation effect on the stomach, with a missing segmentation phenomenon, while RL-Unet and Attention Unet have a better segmentation effect on the stomach. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Methods** & **Aorta** & **Gallbladder** & **Kidney (L)** & **Kidney (R)** & **Liver** & **Pancreas** & **Spleen** & **Stomach** & **DSC** & **HD** \\ \hline ViT [18] & 44.38 & 39.59 & 67.46 & 62.94 & 89.21 & 43.14 & 75.45 & 69.87 & 61.50 & 39.61 \\ U-Net [7] & 89.18 & 65.42 & 76.86 & 70.64 & 93.35 & 55.37 & 89.80 & 76.01 & 77.03 & 39.70 \\ Swin-Unet [20] & 86.34 & 63.45 & 81.13 & 75.69 & 93.63 & 56.83 & 87.94 & 73.22 & 77.28 & 26.93 \\ AttU-Net [10] & 88.59 & 64.42 & 81.73 & 76.77 & 93.99 & 63.68 & 89.56 & 71.18 & 78.74 & 29.54 \\ R2U-Net [42] & 88.26 & 68.97 & 76.94 & 71.36 & 91.86 & 57.36 & 87.36 & 74.88 & 77.12 & 32.12 \\ GCASCADE [28] & 81.55 & 79.75 & 83.06 & 64.46 & 62.89 & 64.43 & 82.87 & 89.39 & 76.05 & 12.34 \\ nnFormer [29] & 92.04 & 70.17 & 86.57 & 86.25 & 96.84 & 83.35 & 90.51 & 86.83 & 86.57 & 10.63 \\ MISSFormer [30] & 86.99 & 68.65 & 85.21 & 82.00 & 94.41 & 65.67 & 91.92 & 80.81 & 81.96 & 18.20 \\ RL-Unet (ours) & 86.95 & 67.48 & 82.68 & 79.47 & 93.84 & 55.97 & 90.21 & 77.41 & 80.13 & 22.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Segmentation results of different methods on the SYNAPSE dataset. Despite the RL-Unet architecture's capability to fuse multi-level semantic features, thereby preserving crucial edge information, its performance in pancreas segmentation remains suboptimal on certain images.