## 2 Related Works
The authors also propose a loss function to guide the overall learning of the combined networks. Some works present modified CNNs or novel architectures to address the multi-center, multi-disease, and multi-view scenarios presented by the M&Ms dataset. The approach developed by [PERSON] et al. [37] utilizes a framework called nnU-Net, designed for automated training of segmentation networks. This framework simplifies the configuration of hyperparameters and enables experimentation with various data augmentation techniques. Benefiting from this, [PERSON] et al. [38] also used the nnU-Net; however, including in the proposed pipeline a step for image synthesis augmentation, which leads them to achieve promising results. [PERSON] et al. [39] also utilized U-Net as a segmentation method in conjunction with a style transfer process using a proposed neural network called the ST network. The training of this network aims to generate a new image as output, obeying a new appearance pattern when given an input image. The U-Net, on the other hand, is trained with images that adhere to this new pattern. According to the authors, this method can be applied to cine-MRI images from different scanners. [PERSON] et al. [40] developed a cascaded method that utilizes two proposed networks called AEM-Net. The first network is used to segment the entire cardiac region encompassing the three structures of interest (LVC, MYO, and RV). This result is then passed to the second network, which simultaneously generates masks for each structure. [PERSON] et al. [41] proposed a network called RegUNet, consisting of three modules: encoder, decoder, and localization. The encoder extracts the semantic features from the input images. Its output is simultaneously passed to the ROI localization module and the decoder. The localization module identifies an ROI encompassing LVC, MYO, and RV. Finally, the decoder generates segmentation using the features extracted by the previous two modules. [PERSON] et al. [42] proposed ARW-Net, which combines residual links and attention gates with the W-Net architecture [43]. In this method, residual connections are incorporated in the encoder part of the network, while attention gates are used in the decoder part. ARW-Net uses a combination of Dice and Cross-entropy losses in a specific proportion (0.8:0.2) selected based on empirical evaluation and experimentation. Similarly to some of the mentioned works, the method proposed by the present work also follows a multi-stage approach, starting with the ROI extraction to crop a region encompassing the LVC, MYO, and RV, and then to a segmentation network, which yields the final masks of those structures. In contrast, the proposed method presents two other stages after the ROI extraction. Thus, the segmentation method is divided into smaller tasks, each based on the specific characteristics of the exams and cardiac structures. In a previous work [44], we developed a cascade method using FCNs. In that work, the extracted ROIs were sent to three different FCNs to perform the initial segmentation of each cardiac structure separately. These results were then sent to a third stage called refinement, in which a reconstruction module based on the use of two U-Nets in addition to image processing techniques to improve the masks generated and provide the final result. However, this method is more complex despite the cascade proposal as it uses six FCNs, one in the ROI extraction, three in the initial segmentation stage, and two more for reconstruction. In addition, the refinement step also comprises image processing techniques parameterized according to the characteristics presented by the images in the ACDC dataset. On the other hand, the proposed method presented in this work was evaluated in a broader context using both ACDC and M&Ms datasets. In this method, the ROIs extracted by the first stage are fed into two different stages, each developed based on a specific segmentation context.