# Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning
## 5 Experiments and Results
It should be mentioned that the ROI models were trained specifically to account for imperfect ROIs, so it is most likely possible to obtain better perfect ROI performance with optimizations in this sense. Promisingly, the performance gained through ensembling is almost as much as through ROI localization, which proves that ensembling fundamentally different architectures is highly beneficial. Specifically, DeepLab differs from U-Net principally in depth, as well as the stride of the contracting path (the value by which the input image dimensions get divided to obtain the smallest feature map: 16 by default for DeepLab, 32 for U-Net). Finally, to officially benchmark our proposed approach, we made two submissions to the ACDC challenge. The first one is the best-performing DeepLab model on the ACDC \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Pretraining** & **Mean Dice** & **StDev** & **Min–Max** \\ \hline \(M_{1}\): DeepLab & No & 85.5 & 2.0 & 82.5–88.1 \\ \(M_{2}\): DeepLab & Yes & 86.6 & 1.9 & 83.4–88.6 \\ \(M_{3}\): Unet & No & 88.2 & 1.2 & 85.8–88.9 \\ \(M_{4}\): Unet & Yes & 88.2 & 1.2 & 85.8–88.9 \\ \(M_{5}\): Unet \& Unet & - & 89.34 & 1.1 & 87.8–90.4 \\ \(M_{6}\): Unet \& DeepLab & - & 88.70 & 0.9 & 87.5–90.0 \\ \(M_{7}\): Standard Ensemble & - & 89.25 & 0.8 & 87.9–90.2 \\ \(M_{8}\): ROI Ensemble & - & 89.41 & 1.2 & 88.0–91.3 \\ \(M_{9}\) : All Ensemble & - & 89.89 & 0.9 & 88.8–91.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results on the imATFIB validation set (Standard Ensemble: Ensemble of best standard Unet and DeepLab, ROI Ensemble: Ensemble of ROI pipelines (\(M_{5}\) and \(M_{6}\)), All Ensemble: Ensemble of the previous two ensembles (\(M_{7}\) and \(M_{8}\)), Ensembling is done by averaging over the predicted softmax values of the models). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Dice RV** & **Dice LV** & **Dice Myo** & **Mean Dice** & **StDev** & **Min–Max** \\ \hline \(M_{1}\): DeepLab & 90.50 & 94.50 & 89.10 & 91.37 & 3.2 & 80.6–95.2 \\ \(M_{3}\): Unet & 89.32 & 95.07 & 89.47 & 91.29 & 3.0 & 83.1–95.5 \\ \(M_{10}\): DeepLab \& DeepLab & 90.58 & 94.72 & 89.53 & 91.61 & 3.4 & 77.5–95.9 \\ \(M_{11}\): DeepLab \& Unet & 90.65 & 95.02 & 89.94 & 91.87 & 3.0 & 81.7–95.7 \\ \(M_{12}\): Standard Ensemble (\(M_{1}\) and \(M_{3}\)) & 90.48 & 95.12 & 89.89 & 91.83 & 3.0 & 82.5–95.9 \\ \(M_{13}\): ROI Ensemble (\(M_{10}\) and \(M_{11}\)) & 90.88 & 94.98 & 90.11 & 91.99 & 3.2 & 80.0–95.9 \\ \(M_{14}\): All Ensemble (\(M_{12}\) and \(M_{13}\)) & 91.26 & 95.39 & 90.49 & 92.38 & 2.9 & 81.7–96.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results on the ACDC validation set (RV—right ventricle, LV—left ventricle, Myo—myocardium). validation set, which is then compared to the All Ensemble. Results are presented in Tables 4 and 5. The Hausdorff metric is a measure of distance between sets (the smaller the better), especially sensitive to outliers, and it is one of the two main metrics of the ACDC challenge (along with the Dice coefficient). Improvements remain consistent with the validation set, and the end result of a mean Dice of 91.87% is decent, being comparable to the second best-performing approach on the challenge [4].