# Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation
## 5 Discussion  
In this work, we proposed a multi-space adaptation-segmentation-joint framework, named the MACS, for generating an accurate segmentation prediction of the unseen target domain. It addresses the challenge of enlarging the model generalizability when the domain shifts. With the domain adaptation in the feature space and the output space, the MACS trained by the source domain dataset generalizes to the target domain dataset. Current echocardiography segmentation methods mainly focus on a single dataset. However, imaging devices and medical centers affect the gap between different datasets. As a result, the current segmentation models trained by one echocardiography dataset cannot be generalized to another dataset. For a single medical center, physicians cannot apply a model trained by other center's datasets to their own images, they have to invest a lot of time labeling their data and retraining the model. This reduces the usability of automated segmentation methods in real clinical diagnosis. The image-to-image translation is a commonly used strategy for cross-center tasks, which can visually bridge the gap between the source and target domain. However, as shown in Figure 6, image-to-image translation methods cannot fully preserve the features and content concerned with the segmentation task when generating medical images. The loss of the segmentation-related information greatly influence the following task. Therefore, directly adapting the domain by image-to-image translation is not able to solve the cross-domain segmentation in echocardiography. Our MACS avoids the interference of the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & & **HD (\(\downarrow\))** & **DI (\(\uparrow\))** & **JI (\(\uparrow\))** \\  & & **mm** & **val.** & **val.** \\ \hline \multirow{2}{*}{Inter-obs} & LV\({}_{\text{endo}}\) & 8.50 & 0.8545 & — \\  & LV\({}_{\text{epi}}\) & 6.45 & 0.9035 & — \\  & LV\({}_{\text{endo}}\) & 4.55 & 0.9375 & — \\  & LV\({}_{\text{epi}}\) & 5.00 & 0.9540 & — \\ DAPIS & mean & 5.26 & 0.9240 & — \\  & LV\({}_{\text{endo}}\) & — & 0.9130 & — \\  & LV\({}_{\text{epi}}\) & — & 0.9400 & — \\ TRSA-Net & LV\({}_{\text{endo}}\) & 0.67 & 0.9543 & — \\  & LV\({}_{\text{epi}}\) & 1.33 & 0.8678 & — \\  & LV\({}_{\text{endo}}\) & 5.07 & 0.9335 & 0.8774 \\  & LV\({}_{\text{epi}}\) & 6.44 & 0.9549 & 0.9143 \\ MACS & LV\({}_{\text{endo}}\) & 6.30 & 0.8764 & 0.7881 \\  & LV\({}_{\text{epi}}\) & 7.59 & 0.9037 & 0.8263 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with SOTA supervised methods on the CAMUS dataset. The up arrow indicates expecting a higher value. information loss via the domain adaptation on the intermediate feature space and output space. The adversarial learning strategy allows the model to learn the segmentation features from the original images. The model can heed the segmentation-related detail of images during the adaptation and generalizes the segmentation performance to the target domain. According to Table 3, the joint framework performs significantly better than the segmentation operation after the domain adaptation is conducted separately. The MACS improves the segmentation performance in the target domain via the adversarial learning between the segmentation network and discriminators. As demonstrated in Table 5, the MACS trained by the self-acquired echocardiography dataset attains the mean DI of 0.89 on the CAMUS dataset, which is better than the inter-observer segmentation consistency offered. It is worth stressing that the MACS achieves such accuracy without the use of the manual label of the CAMUS dataset, while other supervised methods are required to be trained with thousands of annotated images. The results show that the MACS can avoid many of the labeling costs in clinic and create a premise for automatic echocardiography segmentation in clinical applications. The MACS is a practical method for aiding clinical diagnosis.