## Appendix A  
Table 11 shows that Monte-Carlo dropout, spatial augmentation, and style-intensity augmentation together led to the optimal performance of the combined method [17].  
**Figure A1.** Representative segmentation of different regions of the heart at end-systole and end-diastole for the same three subjects as that in Figure 2 using DeepLabV3+. Algorithm and manual segmentation are shown in purple and yellow, respectively. ED: end-diastole; ES: end-systole.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**DSC ([0, 1])**} & \multicolumn{2}{c}{**ASSD (mm)**} \\ \cline{4-7} MCD & **Spa. Aug.** & **Sty-Int. Aug.** & **LVM** & **LVC** & **LVM** & **LVC** \\ \hline ine \(\mathcal{X}\) & \(\mathcal{X}\) & \(\mathcal{X}\) & 0.33 \(\pm\) 0.22 & 0.46 \(\pm\) 0.30 & 16.85 \(\pm\) 21.52 & 15.28 \(\pm\) 18.02 \\ \(\mathcal{X}\) & \(\mathcal{X}\) & \(\mathcal{Y}\) & 0.49 \(\pm\) 0.18 & 0.68 \(\pm\) 0.22 & 8.38 \(\pm\) 7.33 & 8.55 \(\pm\) 8.69 \\ \(\mathcal{X}\) & \(\mathcal{Y}\) & \(\mathcal{X}\) & 0.73 \(\pm\) 0.12 & 0.85 \(\pm\) 0.14 & 2.92 \(\pm\) 2.86 & 3.34 \(\pm\) 3.48 \\ \(\mathcal{X}\) & \(\mathcal{Y}\) & \(\mathcal{Y}\) & 0.77 \(\pm\) 0.07 & 0.87 \(\pm\) 0.11 & 2.39 \(\pm\) 2.05 & 2.80 \(\pm\) 2.49 \\ \(\mathcal{Y}\) & \(\mathcal{X}\) & \(\mathcal{X}\) & 0.34 \(\pm\) 0.21 & 0.49 \(\pm\) 0.29 & 11.30 \(\pm\) 16.93 & 9.97 \(\pm\) 15.36 \\ \(\mathcal{Y}\) & \(\mathcal{X}\) & \(\mathcal{Y}\) & 0.55 \(\pm\) 0.17 & 0.71 \(\pm\) 0.21 & 7.47 \(\pm\) 7.00 & 7.37 \(\pm\) 8.22 \\ \(\mathcal{Y}\) & \(\mathcal{Y}\) & \(\mathcal{X}\) & 0.75 \(\pm\) 0.11 & 0.87 \(\pm\) 0.12 & 2.30 \(\pm\) 1.64 & 2.39 \(\pm\) 1.85 \\ \(\mathcal{Y}\) & \(\mathcal{Y}\) & \(\mathcal{Y}\) & 0.78 \(\pm\) 0.08 & 0.87 \(\pm\) 0.12 & 2.71 \(\pm\) 2.50 & 2.87 \(\pm\) 2.61 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effects of Monte-Carlo dropout, spatial augmentation, and style-intensity augmentation on U-net training using the LVSC dataset. The three components were combined during U-net training (optimal implementation) and the trained U-net models were directly applied to the 90 ACDC test subjects (\(n=180\) images) for DSC and ASSD (mean \(\pm\) SD) calculation.  
**Author Contributions:** Conceptualization, F.G.; methodology, F.G. and M.N; software, F.G.; validation, F.G., M.N. and G.W.; formal analysis, F.G.; investigation, I.R. and G.W.; resources, I.R. and G.W.; writing--original draft preparation, F.G.; writing--review and editing, M.N., I.R. and G.W.; visualization, F.G.; supervision, G.W.; project administration, G.W.; funding acquisition, G.W. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Canadian Institutes of Health Research (CIHR) MOP: #93531, Ontario Research Fund and GE Healthcare. FG is supported by a Banting postdoctoral fellowship.  
The datasets used here were published and made publicly available by previous studies; ethical review and approval were waived for this study.  
Patients' consent was collected in the previous studies and were exempted in this study.  
The data used in this study is publicly available ([[https://www.cardiacitas.org/studies/sunnybrookcardiac-data/](https://www.cardiacitas.org/studies/sunnybrookcardiac-data/)]([https://www.cardiacitas.org/studies/sunnybrookcardiac-data/](https://www.cardiacitas.org/studies/sunnybrookcardiac-data/)), [[https://www.creatis.insalyon.fr/Challenge/acdc/](https://www.creatis.insalyon.fr/Challenge/acdc/)]([https://www.creatis.insalyon.fr/Challenge/acdc/](https://www.creatis.insalyon.fr/Challenge/acdc/)), accessed 20 March 2021).  
We acknowledge the use of the facilities of Compute Canada.  
The authors declare no conflict of interest.