# Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning
## 5 Experiments and Results  
Both the imATFIB and ACDC datasets are split 80/20 into a training and validation set. Evaluation is done at the original resolution of the images. The mean Dice is computed by averaging over the Dice of each volume. For the imATFIB dataset, we measure the Dice on two classes only: heart vs background. An example set of images from the imATFIB dataset is presented in Figure 2. Evaluation scripts provided by the ACDC challenge were used to compute metrics. Table 1 presents reference results for ROI models validated using the ROI computed from the ground truth masks. These results should set a theoretical upper limit to segmentation performance when using ROIs with the models applied, as well as target values to achieve with dynamically computed ROIs. These are the same ROI models that will be used for validation and testing in the following steps. Therefore, any performance drop is certain to be caused by imperfections in computing the ROI boxes. Although no statistically significant differences were observed in terms of the mean Dice value, the U-net is consistently more robust, especially on the ACDC dataset, having a much better result even on the most difficult validation volumes. Tables 2 and 3 show the results obtained on the imATFIB and ACDC validation sets, respectively. On both datasets, the U-net consistently maintains a lower standard deviation and a higher dice value on its worst volumes, which indicates better generalization and more stable performance than the DeepLab model. Furthermore, in Table 2 the last model has the best mean dice value, but also the narrowest Min-Max interval and the second the smallest StdDev. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Dataset** & **Mean Dice** & **StDev** & **Min–Max** \\ \hline DeepLab & ACDC & 92.6 & 5.4 & 67.4– 97.5 \\ Unet & ACDC & 92.9 & 3.6 & 83.8– 97.5 \\ DeepLab & imATFIB & 89.7 & 1.0 & 88.4– 90.8 \\ Unet & imATFIB & 90 & 1.4 & 88.5– 92.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Perfect ROI results on the ACDC and imATFIB validation sets. The imATFIB dataset “mean” refers to the average Dice of all volumes (whole-heart vs background), whereas the ACDC dataset “mean” refers to averaging the dices on the three segmented heart substructures as well (StDev—Standard deviation, Min–Max—range determined by the minimum, respective maximum dice values obtained on the volumes). Figure 2: Sample results from the imATFIB validation set (Red—ground truth, Yellow—model prediction). (**A**) Input image, (**B**) Label mask, (**C**) All Ensemble model (\(M_{9}\)), (**D**) U-net model (\(M_{4}\)). Related to the first four scenarios presented in Table 2, two of them involve a transfer learning approach: starting from a pretrained set of weights obtained by first training on the ACDC dataset, all layers are initially frozen, except the final multi label classification layer (in this case only two labels, heart and background). Layers are then progressively unfrozen up until the 50% of the number of epochs are complete. The training setup is the same as before. Notably, consistent improvements are made via ROI localization and ensembling on both datasets. For ROI extraction, all combinations of model ordering were tested, but only the best-performing ones were written in the tables. The rule for applying ROI extraction is simple, the prediction of the best-performing standard model is used for extraction. This rule determines the Standard Model whose prediction is used for ROI extraction, and then both ROI models are validated with it. Consistent with Table 1, the best results as an ROI model are obtained by the U-Net. Transfer learning did not have a major impact. While bringing an improvement to the performance of the DeepLab, it failed to lead to better convergence for the U-Net. In the case of both datasets, the performance of the ROI segmentation (from computed boxes) worsened minimally compared to the perfect ROI results, which was expected.