## 5 Discussion  
Some strengths of the proposed method can be mentioned. The proposed FCNs (EAIS-Net and IRAX-Net) outperform the U-Net results in the experiments performed with the local test set. In this case, using AIS blocks, combined with other backbones for feature extraction, was pivotal to obtain improvements in contrast to conventional convolutional blocks. In addition, experiments show that the multi-stage approach presented by the proposed method obtains better results than those based on a single-stage process, thus becoming viable in an automated segmentation context.  
Compared with the related works in ACDC and M&Ms challenges, the proposed method achieves competitive results for segmenting cardiac structures. Most of the related work uses variations of U-Net for single-stage segmentation. They have been developed on more robust hardware than the multi-stage approach presented in this work.  
Regarding the experiments with the ACDC dataset, all tests performed show that the proposed method produces better segmentations for the ED slices. In the evaluation on the online platform, this method obtains results that are close to the well-ranked methods in the LVC and MYO segmentation cases. In the experiments with the M&Ms dataset, which presents a context of more significant heterogeneity given the different scanners used to acquire the exams, it is observed that the proposed method presents better results for slices in the ES phase only in the case of MYO. For the other structures, the performance is better with the ED slices.  
The proposed method uses four FCNs in total, a smaller number than the method developed previously [44], which uses six FCNs. The results obtained with the ACDC dataset show that even with the reduction in the number of networks, the Dice values produced are similar, emphasizing the experiments with RV, whose Dice ED of the previous method was surpassed by the method proposed in this work. It was achieved using only the IRAX-Net, whereas the previous method combined the results of FCNs and image processing techniques in the second and third steps to generate the final RV mask.  
The decrease in the number of FCNs combined with other techniques used by the proposed method also reduced the execution time. In experiments with the ACDC dataset for comparison, this method presented an average execution time of 3 s per scan. The MYOand LVC segmentation stage is the slowest, with an average time of 1.6 s per scan because of its substages. In [44], the method completes its entire execution flow in an average of 4 s per scan in the same experiment. In that case, the initial segmentation stage is the most time-consuming, with an average of 2 s per exam. This slowness is due to the execution of three networks, each used to segment a specific cardiac structure.  
It should also be noted that the proposed networks (EAIS-Net and IRAX-Net) have architectures comprising a combination of other backbones with attention mechanisms, and the results obtained were promising compared to other architectures made up of conventional convolution blocks.  
However, the results obtained also indicate some limitations. It is observed that, in most cases, the performance of the proposed method for the ES slices is inferior to that of the ED slices. Specifically, the results for the RV presented lower values than those obtained for the LVC and MYO. The variability of shapes presented by the RV throughout the volume is one of the leading causes. False positives may be yielded from basal slices because the RV is generally occluded by structures with a similar texture. False negatives, in turn, tend to appear in the apical slices because the RV is tiny in this part of the MRI exam.