## 4 Experiments and Results
### Results with the ACDC Dataset
#### 4.3.6 Comparison with Related Works  
The ACDC challenge offers a test set comprising exams without ground truth, just used for the online evaluation process which uses the following metrics: dice coefficient and Hausdorff distance (HD) for a geometric analysis; correlation (C) and bias (B) to measure ED and ES volumes; ejection fraction (EF) and myocardial mass for other clinical purposes. Tables 10-12 show a comparison between the proposed method and the works evaluated in the ACDC challenge. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Dice** & **Dice** & **HD** & **HD** & **EF** & **EF** & **Vol. ED** & **Vol. ED** \\  & **ED** & **ES** & **ED** & **ES** & **C.** & **B.** & **C.** & **B.** \\ \hline
**Proposed Method** & **0.960** & **0.904** & **14.976** & **17.488** & **0.983** & **0.310** & **0.995** & \(-\)**0.710** \\ [PERSON] and [PERSON] [33] & 0.967 & 0.928 & 6.366 & 7.573 & 0.993 & \(-\)0.360 & 0.998 & 2.032 \\ [PERSON] et al. [44] & 0.963 & 0.912 & 8.062 & 10.432 & 0.975 & 1.030 & 0.994 & 0.110 \\ [PERSON] et al. [26] & 0.967 & 0.928 & 5.476 & 6.921 & 0.991 & 0.490 & 0.997 & 1.530 \\ [PERSON] et al. [58] & 0.964 & 0.912 & 6.180 & 8.386 & 0.990 & \(-\)0.476 & 0.997 & 3.746 \\ [PERSON] et al. [59] & 0.961 & 0.911 & 6.152 & 8.278 & 0.990 & \(-\)0.480 & 0.997 & 3.824 \\ [PERSON] et al. [30] & 0.964 & 0.917 & 8.129 & 8.968 & 0.989 & \(-\)0.548 & 0.997 & 0.576 \\ [PERSON] et al. [27] & 0.963 & 0.911 & 6.526 & 9.170 & 0.988 & 0.568 & 0.995 & 1.436 \\ [PERSON] and [PERSON] [31] & 0.958 & 0.903 & 5.592 & 8.644 & 0.981 & 0.494 & 0.997 & 3.072 \\ [PERSON] et al. [60] & 0.961 & 0.918 & 7.515 & 9.603 & 0.988 & \(-\)0.494 & 0.993 & 3.046 \\ [PERSON] et al. [61] & 0.957 & 0.900 & 7.483 & 10.747 & 0.989 & \(-\)0.094 & 0.993 & 4.182 \\ [PERSON] et al. [32] & 0.968 & 0.911 & 7.993 & 10.528 & 0.982 & \(-\)0.390 & 0.997 & 0.650 \\ [PERSON] et al. [42] & 0.967 & 0.938 & 5.652 & 6.878 & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 10: LVC segmentation. The evaluation was performed on the ACDC online platform. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Structure**} & **Proposed Method** & **Dice** & **IoU** \\  & **(ROI Extraction)** & **Dice** & **IoU** \\ \hline LVC & Semi-supervised & 0.9360 & 0.8975 \\ Fully automated & 0.9236 & 0.8851 \\ \hline MYO & Semi-supervised & 0.8655 & 0.7825 \\ Fully automated & 0.8547 & 0.7708 \\ \hline RV & Semi-supervised & 0.8105 & 0.7524 \\ Fully automated & 0.8213 & 0.7623 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experiments performed with the proposed method following two ROI extraction approaches: semi-supervised and fully automated. The proposed method achieves results with values close to those of the top-ranked approaches in the challenge. In the LVC segmentation, this method surpasses the results of Dice ED and ES obtained by more robust approaches, such as AdaEn-Net [31] and the multi-atlas segmentation proposed by [PERSON] et al. [61], the latter being also surpassed in the RV segmentation (Dice ES). The approaches with better results than the proposed method use techniques that demand computational cost and, therefore, more robust hardware, including high-performance graphic cards. The method developed by [PERSON] et al. [26] is outstanding, achieving first place in two out of three scenarios. It is based on an ensemble of 2D and 3D U-Nets. Moreover, their method took less than one second for the 3D model and 1-2 s for the 2D model on a Pascal Titan X GPU. This graphics card has approximately 5.6 times more CUDA cores than the GTX 1050 GPU used in developing our method (3584 versus 640), making it significantly more efficient at processing neural network ensembles and larger dimension images. As mentioned in Section 4.3.4, the average execution time of our proposed method is 3.3 s per exam. In addition, it obtains results close to those of [PERSON] et al. [26] with a difference of 0.007 for Dice ED in LVC segmentation.