## 2 Related Works  
Segmentation in MRI implies discerning which pixels in an image correspond to a determined class given some features, which usually include pixel intensities, textures, and edges. The segmentation performance is generally evaluated using metrics to assess the similarity between a model's prediction and a reference map, such as the Dice coefficient (DC) and the Hausdorff Distance (HD). For example, [PERSON] and [PERSON] [12] presented an approach where multimodal MRI images are processed using various filters to obtain the features that feed a Random Forest (RF) model. The features include the smooth intensitiesgiven by a Gaussian filter, median intensities, edge information, and local entropy. Their approach achieved an average DC and HD of 0.54 and 82.78 for the Ischemic Stroke Lesion Segmentation (ISLES) dataset in its 2015 edition [13]. Although these pixel features led to an acceptable segmentation performance, they are usually processed locally without regard for their context in the whole image. The Deep Learning approach has recently become a remarkable medical imaging advancement [10]. Deep Learning models capture global and local information in the images and integrate them to perform a prediction [14]. This integration allows the evaluation of detailed features, such as edges and textures, in a global context, enhancing the performance in medical image segmentation. However, MRI segmentation presents a high degree of class imbalance when using the overall context of the image, which refers to the disproportion between the number of pixels belonging to the foreground, or segmentation target, and the image's background. Therefore, several studies have addressed this issue, from modifying a network architecture to selecting an adequate loss function. This section presents a comprehensive literature review of deep learning approaches for ischemic stroke segmentation, focusing on the architecture and the loss employed. A point worth mentioning is that the reviewed methods used the same dataset as [PERSON] and [PERSON] [12] for evaluation, which simplifies its comparison. The U-Net architecture is widely used for segmentation purposes. Its layer connections compose a \"U\" shape structure, which allows for preserving spatial information and fine details when the data passes through the encoder and decoder [11], giving precise pixel segmentations. This architecture shape is commonly used as a baseline for different methods. [PERSON] et al. [9] proposed the development of a deep residual attention convolutional neural network (DRANet) model to accurately segment and quantify ischemic stroke lesions and white matter hyperintensities (WMH) in MRI. Their method uses a \"U\"-shape architecture with an attention module to improve predictive performance. This module consists of a main branch and a dilated soft mask branch that work together to enhance the quality of features for accurate segmentation. In addition, the approach employs the Dice loss function, which is based on the DC, to address the class unbalance in the MRI segmentation task. The method was tested on the ISLES 2015 dataset in different MRI modalities, obtaining 0.7178 and 3.36, respectively, for DC and HD in the FLAIR modality. A point worth noting is that the attention modules help the network focus on relevant regions, adding more contextual information. These modules can be added to a U-Net, taking advantage of the intermediate connections, such as in the work of [PERSON] et al. [15] for abdominal CT segmentation. In other work, [PERSON] et al. [16] designed a deep convolution neural network for multimodal MRI segmentation, incorporating dense blocks in a \"U\"-shape-based network. The architecture was trained using a combined loss function based on the Cross-Entropy and Dice loss functions. Their approach achieved an average DC of 0.57 and HD of 43.02 for the multimode input composed of DWI and FLAIR sequences. Another U-Net variant is given by [PERSON] et al. [17].