## 1 Introduction  
The World Health Organization (WHO) states that one of the leading causes of death globally is cardiovascular diseases, or CVDs [1]. They might arise as a result of a sedentary lifestyle, a poor diet, or a hereditary susceptibility, and they cause over 17.5 million deaths per year [2]. Early diagnosis significantly increases the likelihood of successful treatment for CVDs. In this context, cardiac magnetic resonance imaging (CMRI) is a widely used method for diagnosing CVD because it offers a reliable analysis of the anatomy and morphology of cardiac structures [3; 4; 5].  
In the mentioned analysis, the segmentation of the internal and external contours of the cardiac structures is an important step that precedes the quantification of their mass and volume values [6]. However, this process is time-consuming when performed manually by the specialist. Furthermore, it can be tiring and, therefore, susceptible to errors that may lead to an inaccurate diagnosis.  
Computational methods that automate the segmentation process can benefit specialists by providing more accurate analysis. Deep learning-based methods, as fully convolutional networks (FCNs) have achieved interesting results in tasks related to image and video segmentation [7; 8], including medical imaging [9; 10]. FCNs perform image feature extraction from the highest to the lowest level, identifying and extracting hidden patterns that are relevant to classifying pixels as either belonging to structures of interest or not. In addition, these networks have been used for two, three, or more-dimensional medical imaging. However, processing such images can incur a high computational cost, which depends on the task's complexity and the adopted solution.  
The class imbalance between pixels, where most image components do not belong to the object of interest, is another inherent issue in medical imaging [11]. Thus, creating a viable approach that can solve the aforementioned issues and provide reliable results is still a challenge.  
This paper presents a multi-stage method that combines FCNs for cardiac segmentation in short-axis MRI. The three main stages of the method are as follows: (1) extraction of a region of interest (ROI); (2) segmentation of the myocardium (MYO) and left ventricular cavity (LVC) using a coarse-to-fine approach, which combines two FCNs: the proposed EAIS-Net and U-Net; and (3) right ventricle (RV) segmentation using another proposed FCN called IRAX-Net. The final segmentation result is created by combining the outputs of the second and third stages.  
The following points are presented as contributions: (1) a multi-stage method with specific segmentation approaches for each structure of interest; (2) a strategy based on the U-Net to minimize processing costs and class imbalance in the ROI extraction process; (3) the proposal of the EAIS-Net, an FCN that combines EfficientNet [12] with Inception [13], Squeeze-and-Excitation [14], and Attention blocks [15]; (4) the proposal of the IRAX-Net, an FCN that is based on X-Net [16] combined with Inception and Attention blocks.  
The structure of this work is as follows: Section 2 presents some approaches found in the literature; Section 3 introduces the proposed method; Section 4 describes the datasets, experimental setup and the results; Section 5 presents a discussion; and Section 6 shows conclusions and future works.