# Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI
## 4 Results
### Implementation on the PROMISE12 Dataset
#### 4.2.2 Results on the PROMISE12 Test Set  
In this section, the performance of the optimised network was evaluated on the PROMISE12 test set and compared to the state-of-the-art models on the PROMISE12 leaderboard by submitting the model predictions to the MICCAI PROMISE12 grand-challenge website where the scores are generated by the organiser. Here, the test set mean DSC and PROMISE12 overall score [36] are used for the evaluation of the models. The performance results of different methods as well as the model type, preprocessing and postprocessing details are presented in the Table 4. Further details of the results can be obtained from the MICCAI PROMISE12 grand-challenge website [36].  
In Table 4, the optimised model is compared with the top 12 state-of-the-art 2D models on the PROMISE12 leaderboard, all the 3D and combination of the 2D/3D models have been excluded from the table as they are not directly comparable to the model structure in this paper. From the models shown in the table, we will discuss in the sequel why some of these should be excluded from a direct comparison with our model due to a number of factors such as enhancing pre/post-processing and a stacked model structure.  
Figure 7: Prostate segmentation results by the optimised model. Model prediction in red, while the ground truth in green. (**a**) Prediction with DSC score of 0.94; (**b**) Prediction with DSC score of 0.73.  
As shown, both of the Z-Nets perform better as compared to our optimised model in terms of both mean DSC and overall score. However, both Z-Nets use either enhancing pre-processing or post-processing to improve the model performance. Therefore, the Z-Nets results are incomparable with our optimised model as we do not employ any enhancing pre- or post-processing. On the other hand, W-Net does not use any enhancing pre- or post-processing and it has a mean DSC of 0.8996 and overall score of 86.5028. However, W-Net is a stacked U-Net, i.e., it utilises a double U-Net to perform the segmentation, hence it is not comparable with our optimised model that consists of a single U-Net. Similar with our approach, sho89512 uses a single U-Net structure model and does not employ any enhancing pre- or post-processing method. However, although it performs better in the overall score (86.3676 vs. 85.4954), it performs slightly worse than the optimised model in the mean DSC (0.8898 vs. 0.89). As can be seen from Table 4, our optimised model performs significantly better compared to the rest of the 2D U-Net-based models with (e.g., MBIOS, UdeM 2D and AdaResU-Net) or without (e.g., U-Net with dense block and U-Net with skip connection) the use of enhancing pre- and post-processing. Note that, it also performs better than the adversial network.  
Most importantly, the optimised model is seen to be better than U-Net (MBIOS), U-Net with residual connection (Udem 2D) and U-Net with skip connection (by wanlichen (WNet)) as a result of the optimisation. Another key point to note is that the optimised model performs better than the U-Net with dense block (by pppppppjw), and only slightly worse than U-Net with dense dilated block (by sho89512 and fumin).