# Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?
## 4 Results and Discussion
For example, when comparing the Non-Expert 1 results with MAE+Dice loss and those from the expert annotation, we see that the Dice difference for the RV decreases from a 6% on the ACDC  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**U-Net** & & & & & \\ \hline
**Test Set** & **ACDC Trainset** & **Loss** & **Avg. DSC** & **Avg. HD** & **Avg. Mass Err. ** \\ \hline \multirow{4}{*}{ACDC} & **Expert** & CE+Dice & \(0.88\pm 0.03\) & \(11.02\pm 6.25\) & \(16.88\pm 9.66\) \\  & Non-Expert 1 & \(\left\{\begin{matrix}\text{CE+Dice}&0.82\pm 0.  
data set to a mere 4% on the M&Ms data set; overall, the results of Non-Expert 2 are similar (and sometimes better) than those of the expert. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**U-Net** & & & & & & & \\ \hline
**Test Set** & \begin{tabular}{c} **ACDC Train** \\ **set** \\ \end{tabular}  & **Loss** & **Avg. DSC** & **Avg. HD** & **Avg. EF (\%)** & **Avg EF Err. ** & **Avg. EDV (mL)** \\ \hline \multirow{3}{*}{ACDC} & **Expert** & CE+Dice & \(\mathbf{0.90\pm 0.06}\) & \(14.72\pm 6.12\) & \(42.77\pm 15.25\) & \(\mathbf{6.69\pm 6.21}\) & \(183.73\pm 70.68\) \\  & Non-Expert 1 & \(\left\{ \begin{array}{l}\text{CE+Dice}\\ \text{MAE+Dice}\\ \text{MAE+Dice}\\ \end{array} \right.\) & \(0.78\pm 0.11\) & \(19.59\pm 8.07\) & \(32.22\ ^{\ \pm}\pm 13.49\) & \(14.77\ ^{\ \pm}\pm 9.88\) & \(216.16\ ^{\ \pm}\pm 69.96\) \\  & Non-Expert 2 & \(\left\{ \begin{array}{l}\text{CE+Dice}\\ \text{MAE+Dice}\\ \end{array} \right.\) & \(0.84\pm 0.09\) & \(16.78\pm 7.23\) & \(35.51\ ^{\ \pm}\pm 14.64\) & \(13.52\ ^{\ \pm}\pm 8.88\) & \(181.61\ ^{\ \pm}\in 61.85\) \\  & Non-Expert 2 & \(\left\{ \begin{array}{l}\text{CE+Dice}\\ \text{MAE+Dice}\\ \end{array} \right.\) & \(0.86\pm 0.07\) & \(15.25\pm 6.00\) & \(40.10\pm 14.42\) & \(7.98\pm 7.34\) & \(200.67\ ^{\ \pm}\pm 73.35\) \\  & Non-Expert 1 & \(\left\{ \begin{array}{l}\text{CE+Dice}\\ \text{MAE+Dice}\\ \end{array} \right.\) & \(0.89\pm 0.07\) & \(\mathbf{14.65\pm 6.34}\) & \(41.61\pm 14.85\) & \(7.38\pm 7.27\) & \(186.59\pm 71.56\) \\ \hline \multirow{3}{*}{M& **Expert** & CE+Dice & \(\mathbf{0.82\pm 0.15}\) & \(\mathbf{16.91\pm 12.65}\) & \(52.90\pm 19.79\) & \(\mathbf{9.12\pm 12.73}\) & \(145.92\pm 61.94\) \\  & Non-Expert 1 & \(\left\{ \begin{array}{l}\text{CE+Dice}\\ \text{MAE+Dice}\\ \end{array} \right.\) & \(0.74\pm 0.15\) & \(22.51\pm 15.82\) & \(37.68\ ^{\ \pm}\pm 21.06\) & \(17.88\ ^{\ \pm}\pm 18.47\) & \(173.34\ ^{\ \pm}\pm 70.88\) \\  & Non-Expert 2 & \(\left\{ \begin{array}{l}\text{CE+Dice}\\ \text{MAE+Dice}\\ \end{array} \right.\) & \(0.78\pm 0.15\) & \(16.93\pm 10.95\) & \(38.75\ ^{\ \pm}\pm 36.59\) & \(18.12\ ^{\ \pm}\pm 33.08\) & \(144.36\ ^{\ \pm}\in 60.57\) \\  & Non-Expert 2 & \(\left\{ \begin{array}{l}\text{CE+Dice}\\ \text{MAE+Dice}\\ \end{array} \right.\) & \(0.79\pm 0.16\) & \(20.31\pm 17.56\) & \(44.41\ ^{\ \pm}\pm 29.89\) & \(12.86\ ^{\ \pm}\pm 26.88\) & \(168.76\ ^{\ \pm}\in 66.89\) \\  & Mae+Dice & \(0.81\pm 0.17\) & \(18.05\pm 15.60\) & \(39.17\ ^{\ \pm}\pm 91.83\) & \(20.72\ ^{\ \pm}\pm 88.32\) & \(149.17\ ^{\ \pm}\in 66.41\) \\ \hline \multirow{3}{*}{**Attention**} & & & & & & & \\ \cline{2-8}
**U-Net** & & & & & & & & \\ \cline{2-8}
**Test Set** & \begin{tabular}{c} **ACDC Train** \\ **set** \\ \end{tabular}  & **Loss** & **Avg. DSC** & **Avg. HD** & **Avg. EF (\%)** & **Avg EF Err. ** & **Avg. EDV (mL)** \\ \hline \multirow{3}{*}{ACDC} & **Expert** & CE+Dice & \(\mathbf{0.89\pm 0.07}\) & \(16.62\pm 7.71\) & \(38.73\pm 15.58\) & \(8.71\pm 7.46\) & \(194.05\pm 71.01\) \\  & Non-Expert 1 & \(\left\{ \begin{array}{l}\text{CE+Dice}\\ \text{MAE+Dice}\\ \text{MAE+Dice}\\ \text{MAE+Dice}\\ \end{array} \right.\) & \(0.79\pm 0.10\) & \(20.60\pm 9.19\) & \(30.12\ ^{\ \pm}\pm 13.11\) & \(15.96\ ^{\ \pm}\pm 9.27\) & \(218.32\ ^{\ \pm}\in 69.08\) \\  & Non-Expert 2 & \(\left\{ \begin{array}{l}\text{CE+Dice}\\ \text{MAE+Dice}\\ \text{MAE+Dice}\\ \end{array}