# Detecting Small Anatomical Structures in 3D Knee MRI Segmentation by Fully Convolutional Networks
## 4 Experiments and Results
### Results with DeepLabv3 plus Variants  
Because it is confirmed that SE structure can improve the performance of the networks, we further train the networks with SE blocks for both residual structure and dense structure in DeepLabv3 plus. As is shown in Table 3, two variants are trained first, including one in which the basic network uses residual block and one in which the basic network uses dense block. We found that adding SE block can improve the two DeepLabv3 plus baselines without SE block. As shown in Table 3 and Figure 5, we also find that DeepLabv3 plus variants can segment the structures better than U-net variants. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**Dice Loss**} & \multicolumn{3}{c}{**Weighted Dice Loss**} \\ \cline{2-7}  & **U-Net** & **U-Net + Res** & **U-Net + Res** & **U-Net + Res + SE** & **U-Net + Dense** & **U-Net + Dense + SE** \\ \hline BG & 0.94 & 0.987 & 0.992 & 0.989 & 0.990 & 0.990 \\ BO & 0.95 & 0.917 & 0.831 & 0.938 & 0.932 & 0.948 \\ PCL & 0 & 0.002 & 0.153 & 0.347 & 0.338 & 0.228 \\ ACL & 0 & 0 & 0.083 & 0.096 & 0.084 & 0.041 \\ MU & 0.95 & 0.927 & 0.944 & 0.956 & 0.928 & 0.939 \\ CB & 0 & 0.567 & 0.500 & 0.457 & 0.495 & 0.515 \\ BV & 0 & 0.556 & 0.622 & 0.603 & 0.555 & 0.648 \\ AR & 0 & 0.057 & 0.198 & 0.270 & 0.152 & 0.194 \\ CL & 0 & 0.041 & 0.470 & 0.248 & 0.237 & 0.370 \\ TE & 0 & 0.726 & 0.701 & 0.723 & 0.641 & 0.726 \\ ME & 0 & 0.191 & 0.133 & 0.126 & 0.139 & 0.107 \\ AD & 0.92 & 0.919 & 0.876 & 0.919 & 0.904 & 0.925 \\ VE & 0 & 0 & 0.396 & 0.491 & 0.305 & 0.528 \\ Avg-All organs & 0.289 & 0.453 & 0.531 & 0.551 & 0.515 & 0.551 \\ Avg-Small organs & 0 & 0.238 & 0.362 & 0.373 & 0.327 & 0.373 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performances of U-net variants. Where (a) shows the segmentation of the results of U-Net + Res with dice loss, (b) and (c) show the results of DeepLabv3 plus variants with dice loss. We can see that, for example, the green part (PCL) in the middle is not segmented correctly in (a), but it is segmented in (b) and (c). The results are shown in Table 2. With dice loss, the accuracies of small anatomical segments are quite acceptable compared with the results on U-Net + Res. One reason could be that advanced components, such as ASPP, are used to obtain a larger receptive field without losing too much resolution. Generally speaking, Deeplab + Dense + SE performs better than Deeplab + Res + SE. Deeplab + Dense + SE has higher average accuracies on small structures, especially in PCL, BV, and CL. Deeplab + Res + SE has slightly higher accuracies on ACL, MU, and CB. Figure 6b shows the performance matrix of Deeplab + Dense + SE, where we can see it achieved relatively high accuracies on all anatomical segments compared with U-Net + Res (as it is shown in Figure 6a). For U-Net + Res with dice loss, small structures, including PCL(2), ACL(3), AR(7), and CL(8) were missing, and predicted as other structures such as BG(0), CB(5), and BV(6). An example of the segmentation result of Residual SE Deeplab with dice loss is shown in Figure 5c, where we can see that there is a small part of AD(11) predicted as BO(1), similar to the results of Dense SE Deeplab with dice loss. A possible reason could be that patch-wise training that negatively influences the capture ability of structural features of bones. However, this problem has already been solved largely in DeepLabv3 plus variants.