## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**DSC ([0, 1)**)} & \multicolumn{2}{c}{**ASSD (mm)**} \\ \cline{2-5}  & **LVM** & **LVV** & **LVM** & **LVC** \\ \hline Proposed & 0.81 \(\pm\) 0.09 & 0.90 \(\pm\) 0.09 & 2.04 \(\pm\) 1.77 & 1.82 \(\pm\) 2.18 \\ Naive & 0.74 \(\pm\) 0.12 * & 0.87 \(\pm\) 0.12 * & 2.43 \(\pm\) 2.16 * & 2.40 \(\pm\) 2.58 * \\ Combined & 0.78 \(\pm\) 0.08 * & 0.87 \(\pm\) 0.12 * & 2.71 \(\pm\) 2.50 * & 2.87 \(\pm\) 2.61 * \\ DeepLab & 0.26 \(\pm\) 0.18 * & 0.32 \(\pm\) 0.27 * & 18.60 \(\pm\) 17.48 * & 17.33 \(\pm\) 12.37 * \\ \hline \hline \end{tabular} DSC: The-similarity-coefficient, ASSD: average-symmetric-surfaced-distance; LVM: left ventricle myocardium, LVC: left ventricle cavity; *: \(p\) < 0.0001 when compared with the proposed algorithm.  
\end{table} 
Table 1: LV myocardium and cavity segmentation accuracy (mean \(\pm\) SD) for \(n\) = 180 images from 90 previously unseen ACDC test subjects. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Manual** & **Proposed** & **Naive** & **Combined** & **DeepLab** \\ \hline LVMM (g) \({}^{\forall}\) & 138.1 \(\pm\) 54.3 & 129.3 \(\pm\) 49.80\({}_{1976}\) & 110.8 \(\pm\) 48.2\({}_{<0.0001}\) & 154.4 \(\pm\) 83.6\({}_{<0.0023}\) & 46.4 \(\pm\) 34.7\({}_{<0.0001}\) \\ LVEDV (mL) & 163.8 \(\pm\) 75.2 & 162.9 \(\pm\) 72.0\({}_{0.8015}\) & 174.6 \(\pm\) 74.5\({}_{<0.0001}\) & 175.8 \(\pm\) 72.8\({}_{<0.0001}\) & 71.8 \(\pm\) 69.3\({}_{<0.0001}\) \\ LVESV (mL) & 99.4 \(\pm\) 80.4 & 99.2 \(\pm\) 76.7\({}_{0.8631}\) & 108.2 \(\pm\) 80.0\({}_{<0.0001}\) & 118.4 \(\pm\) 76.2\({}_{<0.0001}\) & 58.3 \(\pm\) 62.1\({}_{<0.0001}\) \\ LVSV (mL) & 64.4 \(\pm\) 24.6 & 63.7 \(\pm\) 25.8\({}_{0.6617}\) & 66.5 \(\pm\) 31.5\({}_{0.0734}\) & 57.4 \(\pm\) 25.9\({}_{<0.0001}\) & 13.5 \(\pm\) 24.2\({}_{<0.0001}\) \\ LVEF (\%) & 46.2 \(\pm\) 20.4 & 45.5 \(\pm\) 20.5\({}_{0.2495}\) & 43.0 \(\pm\) 23.6\({}_{0.0059}\) & 36.7 \(\pm\) 18.4\({}_{<0.0001}\) & 142.2\({}_{<0.0001}\) \\ \hline \hline \end{tabular} LVMM: LV myocardium mass (g), LVEDV: LV end-distalc volume (mL); LVESV: LV end-systolic volume (mL); LVSV: LV stroke volume (mL); LVEF: LV ejection fraction (%); \({}^{\forall}\): \(n\) = 180 images from 90 subject; \(p\)-values for comparison of algorithm vs. manual LV indices are shown in subscripts.  
\end{table} 
Table 2: Algorithm and manual LV function measurements (mean \(\pm\) SD) for \(n\) = 180 images from 90 previously unseen ACDC test subjects. Figure 3 also shows the quantitative agreement between the proposed algorithm and manual LV indices. Bland-Altman analyses indicated that there was promising agreement between the proposed algorithm and manual LVMM (bias = \(-\)8.8 \(\pm\) 30.3 g, 95% LOA = [\(-\)68.1, 50.5] g), LVEDV (bias = \(-\)0.9 \(\pm\) 13.1 mL, 95% LOA = [\(-\)26.6, 24.8] mL), LVESV (bias = \(-\)0.2 \(\pm\) 13.8 mL, 95% LOA = [\(-\)27.3, 26.9] mL), LVSSV (bias = \(-\)0.7 \(\pm\) 10.0 mL, 95% LOA = [\(-\)20.2, 18.9] mL), and LVEF (bias = \(-\)0.6 \(\pm\) 7.8%, 95% LOA = [\(-\)15.9%, 14.6%]). In contrast, the naive and combined methods yielded greater biases and wider 95% LOAs for LVMM (Naive: bias = \(-\)27.3 \(\pm\) 33.3 g, 95% LOA = [\(-\)92.5, 37.9] g; Combined: