# Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI
## 5 Discussion  
In deep learning applications, the model architecture tends to be the main focus as it defines the capability and capacity of a network to extract features and learn. Often a very complicated model is developed (e.g., stacked, ensemble, hybrid, etc.) without understanding the full capability of a single U-Net structure and the components within. For a deep learning model with supervised learning, data quantity and label quality should also be of a primary focus. For example, it is known that medical imaging data labelling is always subject to intra- and inter-expert variability, that cannot be avoided, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Rank** & **Team** & **Model** & \begin{tabular}{c} **Model** \\ **Type** \\ \end{tabular} & \begin{tabular}{c} **Pre-** \\ **Processing** \\ \end{tabular} & \
\begin{tabular}{c} **Post-** \\ **DSC (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Mean** \\ **Score** \\ \end{tabular} \\ \hline
35 & u3004443 & Z-Net & Single & Yes & Yes & 90.50 & 87.8068 \\ \hline
59 &
\begin{tabular}{c} hkuandrewzhang \\ (Revised\_U-net) \\ \end{tabular} & Z-Net & Single & Yes & No & 90.24 & 87.3217 \\ \hline
86 & wanlichen (WNet) & W-Net [60] & Stacked & No & No & 89.96 & 86.5028 \\ \hline
92 & sho89512 &
\begin{tabular}{c} U-Net w/ \\ Dense Dilated Block \\ \end{tabular} & Single & No & No & 88.98 & 86.3676 \\ \hline
95 & fumin &
\begin{tabular}{c} RUCIMS (U-Net w/ \\ Dense Dilated Block) \\ \end{tabular} & Single & Yes & No & 88.75 & 86.2589 \\ \hline
**122** & **Indri92 (This paper)** & **UNet\_S.2.0.1.1 (U-Net)** & **Single** & **No** & **No** & **89.00** & **85.4954** \\ \hline
140 & ddd52317102008 & Adversarial Network & Adv. Net. & No & No & 87.90 & 84.5935 \\ \hline
163 & mirzaevinom & MBIOS (U-Net) & Single & Yes & No & 88.06 & 83.6633 \\ \hline
167 & pppppppjw & U-Net w/ Dense Block & Single & No & No & 86.80 & 83.5027 \\ \hline
168 & michaldrozdzal & UdeM 2D (ResNet) & Stacked & No & Yes & 87.42 & 83.4522 \\ \hline
179 & mariabaldeon & AdaResU-Net [61] & Single & Yes & No & 86.51 & 82.7937 \\ \hline
194 & wanlichen (WNet) &
\begin{tabular}{c} U-Net w/ \\ skip connection \\ \end{tabular} & Single & No & No & 86.29 & 82.1644 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison between the optimised model and state-of-the-art models on the PROMISE12 test set. hence larger data sets are required in order to obtain the best result. To highlight the inter-expert variability problem, we show the inter-expert DSC score (between experts 1 and 2, 1 and 3, and 2 and 3) for the segmentation of the prostate in the box-and-whisker plot, Figure 7(a), of the private dataset where it can be observed that the range of variability is very large. Also, it can be seen from the box-and-whisker plot in Figure 7(b) that the optimised model is shown to perform better than any given expert and almost as well as the other experts with respect to the majority voting label. In future work, we suggest that an automatic segmentation model could be used to provide basic guidance for the expert to produce a larger dataset so that a more robust model can be developed. Furthermore, a combination of Multiparametric MRI (such as T1w, T2w, ADC and PDw) can be considered as input to the neural network model to provide more initial features (i.e., information) for the network to perform the segmentation as it has been shown to be significantly beneficial in prostate segmentation [27; 62] and prostate cancer detection [63]. Furthermore, integration of Attention Gates (AGs) [64] and Squeeze-and-Excitation (SE) blocks [65; 66] are shown to increase the performance of the U-Net model in performing segmentation [64; 66] and could be considered as another component for optimisation in the U-Net structure as performed in this paper.