## 2 Related Works  
This Section aims to present the related works that served as a reference for developing the study on cardiac segmentation in short-axis CMRI using convolutional neural networks. Some U-Net-based methods utilize locally acquired datasets [17; 18], which impedes both the reproducibility and comparative analysis of results. Public datasets, such as the Sunnybrook Cardiac Dataset [19] and the LVSC [20], are used in many works employing FCNs [21; 22] or architectures incorporating Attention Gates in their architecture [23] to perform segmentation in a single-stage process. However, the datasets mentioned above are focused on segmenting the LV contours. On the other hand, the ACDC [24] and M&Ms [25] datasets are aimed at segmenting the LVC, MYO, and RV, which is the primary goal of this paper. Therefore, this section presents the works that use these datasets in more detail. Starting with the ACDC dataset, a combination of 2D and 3D U-Nets was proposed by [PERSON] et al. [26] for a single-process segmentation. Along the same line, [PERSON] et al. [27] carried out experiments using 2D and 3D U-Net, and the FCN-8s, maintaining specific input slice dimensions to preserve information. However, the 2D and 3D models that are used require input slices with large width and height values, resulting in a computationally intensive process that may not always be feasible. The study by [PERSON] et al. [28] shows a comparative analysis of CNN and U-Net performance for cardiac MRI segmentation. During training, the authors explored data augmentation techniques, including morphological operations. A hyperparameter estimation process was also investigated, focusing on learning rate, number of epochs, and optimization algorithms such as ADAM and RMSProp to identify the most effective approach for optimizing model parameters. Modified FCN architectures were proposed to address the cardiac segmentation. [PERSON] et al. [29] introduced GridNet, an architecture derived from U-Net that replaces the skip connections with convolutional layers to enhance the number of relevant feature maps. [PERSON] et al. [30] presented an approach called multi-scale residual dense-net used to produce many feature maps while avoiding the gradient explosion problem. [PERSON] and [PERSON] [31] introduced AdaEn-Net, an adaptive ensemble comprising 2D and 3D FCNs. The adaptation process proposed involves running a multi-objective optimization algorithm to determine the network's hyperparameters. Multi-stage approaches were also developed. The method proposed by [PERSON] et al. [32] has an ROI extraction process that employs fast Fourier transform (FFT) to locate the heart region. Then, the extracted ROIs are passed to the conventional U-Net for segmentation, followed by a refinement step focused on the MYO masks. Similarly, [PERSON] and [PERSON] [33] proposed a two-stage method. However, the ROI extraction is based on the work of [PERSON] and [PERSON] [34]. The second step uses a dilated convolutional neural network (DCNN) for segmentation. Among the works related to the M&Ms dataset, many approaches based on U-Net and its variations are found. The method developed by [PERSON] et al. [35] presents an approach called domain-adversarial learning, which aims to enhance the generalization of neural networks by ensuring that they learn features independently of the input domain. During the training of a 2D U-Net for cardiac structure segmentation, its intermediate feature maps are passed to a CNN optimized to classify the input domains. Finally, the parameters of the U-Net are updated via adversarial back-propagation. Adversarial learning is also explored by [PERSON] et al. [36] in a proposed semi-supervised method that combines U-Net and Conditional GAN (CGAN) as an autoencoder. In this process, the images are pre-cropped to a size of \(160\times 160\) and registered using a chosen reference image to align the centers of the LVC. The U-Net initially produces masks, which are fed into the CGAN framework's generator network.