# Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation
## 4 Result
### Comparison with Joint Adversarial Learning Models  
To confirm the effectiveness of our method, the MACS is compared with the state-of-the-art adversarial learning methods: p-SOAL [21] and BEAL [22]. Considering the area occupied by the left ventricle in the echocardiography is not exiguous, we remove the ROI extraction module of p-SOAL. All of the adversarial learning models are trained with the CAMUS training set and 400 unlabeled images from the self-acquired dataset, and are then tested on 384 rest images of the self-acquired dataset. Table 4 shows the segmentation results of the quantification. Comparing the result from the simple U-Net, all of the adversarial learning methods improve the generalizability of the segmentation network. Our MACS performs better on LV\({}_{\text{endo}}\) segmentation than the other two adversarial learning methods. Figure 7 presents the Bland-Altman analysis of our MACS and other adversarial learning methods on the segmentation results of the self-acquired dataset. The Bland-Altman plots reflect the consistency of the segmentation results and the ground truth. In each subplot, the forest green or royal blue line indicates the average bias of the organ area, and the brown lines indicate 95% confidence intervals (CI) of the area bias. For our method, the average bias of the LV\({}_{\text{endo}}\) and LV\({}_{\text{epi}}\) areas are 0.45 and 2.8 compared with the ground truth. The plots show that the average bias of the MACS is less than the other two methods and the segmentation for the LV\({}_{\text{endo}}\) has a higher performance than for the LV\({}_{\text{epi}}\). Figure 8 displays the representative segmentation performance on the self-acquired dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**HD (\(\downarrow\))**} & **DI (\(\uparrow\))** & **JI (\(\uparrow\))** \\ \cline{2-5}  & **mm** & **val.** & **val.** \\ \hline \multirow{2}{*}{U-Net} & LV\({}_{\text{endo}}\) & 6.47 & 0.8457 & 0.7462 \\  & LV\({}_{\text{epi}}\) & 8.10 & 0.8630 & 0.7675 \\ \hline \multirow{2}{*}{p-OSAL} & LV\({}_{\text{endo}}\) & 5.68 & 0.8982 & 0.8178 \\  & LV\({}_{\text{epi}}\) & 7.68 & 0.8734 & 0.7820 \\ \hline \multirow{2}{*}{BEAL} & LV\({}_{\text{endo}}\) & 5.72 & 0.9030 & 0.8260 \\  & LV\({}_{\text{epi}}\) & 7.29 & 0.8865 & 0.8021 \\ \hline \multirow{2}{*}{MACS} & LV\({}_{\text{endo}}\) & 5.65 & 0.9033 & 0.8265 \\  & LV\({}_{\text{epi}}\) & 7.64 & 0.8792 & 0.7910 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with SOTA adversarial learning methods. The up arrow indicates expecting a higher value. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**LV\({}_{\text{endo}}\)**} & \multicolumn{2}{c}{**LV\({}_{\text{epi}}\)**} \\ \cline{2-7}  & **HD (\(\downarrow\))** & **DI (\(\uparrow\))** & **JI (\(\uparrow\))** & **HD (\(\downarrow\))** & **DI (\(\uparrow\))** & **JI (\(\uparrow\))** \\ \cline{2-7}  & **mm** & **val.** & **val.** & **mm** & **val.** & **val.** \\ \hline SAGAN & 6.03 & 0.8690 & 0.7715 & 7.28 & 0.9174 & 0.8419 \\ F-LSeSim & 6.60 & 0.8264 & 0.7125 & 7.93 & 0.8669 & 0.7707 \\ CUT & 7.01 & 0.7571 & 0.6236 & 8.34 & 0.8171 & 0.7020 \\ MACS & 5.79 & 0.8847 & 0.7963 & 7.11 & 0.9167 & 0.8475 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with the segmentation performance on images translated by the SAGAN, F-LSeSim and CUT. The up arrow indicates expecting a higher value. Figure 8: Representative visual segmentation results of adversarial learning methods. Green and blue contours indicate boundaries of the LV\({}_{\text{endo}}\) and LV\({}_{\text{epi}}\), respectively. Yellow and orange colors denote the ground truth. Figure 7: The Bland-Altman analysis on adversarial learning methods about the organ area (\(\times 10^{3}\) pixel\({}^{2}\)). The x-axis represents the average area of the prediction and the ground truth (GT). The y-axis represents the bias of the area of the prediction and the GT.