# Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?
## 4 Results and Discussion
\right.\) & \(0.83\pm 0.09\) & \(20.08\pm 11.27\) & \(33.00\ ^{\ \pm}\pm 14.62\) & \(13.57\ ^{\ \pm}\pm 9.68\) & \(198.36\ ^{\ \pm}\in 68.13\) \\  & Non-Expert 2 & \(\left\{ \begin{array}{l}\text{CE+Dice}\\ \text{MAE+Dice}\\ \end{array} \right.\) & \(0.86\pm 0.07\) & \(16.33\pm 7.92\) & \(37.96\pm 15.19\) & \(9.20\pm 7.15\) & \(200.98\pm 71.70\) \\  & Mae+Dice & \(0.88\pm 0.09\) & \(\mathbf{16.30\pm 9.18}\) & \(39.65\pm 15.58\) & \(\mathbf{8.31\pm 8.28}\) & \(185.30\pm 70.51\) \\ \hline \multirow{3}{*}{M& **Expert** & CE+Dice & \(\mathbf{0.82\pm 0.15}\) & \(\mathbf{17.94\pm 12.54}\) & \(50.23\pm 15.46\) & \(\mathbf{9.39\pm 8.21}\) & \(156.45\pm 62.88\) \\  & Non-Expert 1 & \(\left\{ \begin{array}{l}\text{CE+Dice}\\ \text{MAE+Dice}\\ \text{MAE+Dice}\\ \end{array} \right.\) & \(0.77\pm 0.14\) & \(20.96\pm 11.07\) & \(37.69\ ^{\ \pm}\in 19.13\) & \(17.86\ ^{\ \pm}\in 15.87\) & \(179.85\ ^{\ \pm}\in 67.54\) \\  & Non-Expert 2 & \(\left\{ \begin{array}{l}\text{CE+Dice}\\ \text{MAE+Dice}\\ \text{MAE+Dice}\\ \end{array} \right.\) & \(0.77\pm 0.14\) & \(24.24\pm 14.61\) & \(37.94\ ^{\ \pm}\in 18.12\) & \(17.67\ ^{\ \pm}\in 15.47\) & \(167.74\ ^{\ \pm}\in 60.49\) \\  & Non-Expert 2 & \(\left\{ \begin{array}{l}\text{CE+Dice}\\ \text{MAE+Dice}\\ \text{MAE+Dice}\\ \end{array} \right.\) & \(0.79\pm 0.15\) & \(18.09\pm 13.02\) & \(47.28\ ^{\ \pm}\in 19.16\) & \(12.20\ ^{\ \pm}\in 12.84\) & \(159.95\pm 64.95\) \\  & Mae+Dice & \(0.80\pm 0.17\) & \Figure 3: Dice score on ACDC and M&Ms data set per anatomical structure and per slice location. Figure 2: Examples of bad output segmentation on the M&Ms data set for the left ventricle, right ventricle and myocardium. between the CE+Dice and the MAE+Dice losses and observed that the MAE+Dice loss provides overall better results for both non experts. Overall on M&Ms, while the expert obtained a better MYO mass error and a better RV EF error, the MYO HD was lower for Non-Expert 2 and the Dice score and the RV HD of Non-Expert 2 are statistically similar. These results underline the idea that well-trained non-expert and expert annotations could be used interchangeably to build reliable annotated cardiac data sets. In contrast, the number of non-experts we evaluated might be considered a limitation of our study; however, this still provides encouraging results for settings where experts are not readily available to annotate whole data sets, but can provide training to a non-expert to effectively annotate in their stead. We leave the investigation on more data sets to future works that could transpose the setup to more difficult problems and a larger number of non-experts. Our work supplements previous endeavors that rely on non-experts to annotate medical data sets; [PERSON] et al. (2020) showcased the ability of crowd-sourced expertise to reliably annotate the liver data set, although their approach proposes initial segmentations to the non-expert, which might have biased their decision. Likewise, [PERSON] et al. (2021) proposed to make use of non-experts as a crowd-sourced error detection framework. In contrast, our approach evaluated the effectiveness of non-expert knowledge without any prior input. This further reinforces the idea that crowd-sourced medical annotations are a viable solution for the lack of data.