## 2 Related Works
Their technique uses five convolutional blocks in the encoder and a Leaky Rectified Linear Unit (ReLU) as the activation function in the final layers. Their study compares the model's performance using Adaptive Moment Estimation (ADAM), AdaGrad and AdaDelta optimizers and the Cross-Entropy, Dice, and Tversky loss functions. The model was trained and tested on multimodal MRI datasets, obtaining the best results in the ADAM optimizer and Dice loss version, representing an average DC score of 0.7008. Similarly, [PERSON] et al. [18] proposed a modified U-Net architecture that includes inception blocks, which provide multiple connection paths to give flexibility in the complexity level of the encoder and decoder. Their study evaluated their model using FLAIR, DWI, T1, and T2 modalities, with FLAIR having the highest DC at 0.7466. Moreover, the U-Net has also been utilized with a transfer learning approach. [PERSON] et al. [19] employed a pre-trained ResNet50 architecture combined with a U-Net. The ResNet50 backbone was included in the U-Net encoder, and a Convolutional Block Attention Module (CBAM) was added after the convolution blocks. The model was fine-tuned using Stochastic Gradient Descent (SGD) and Categorical Cross-Entropy loss. The model was evaluated on DWI and T2 images, and an average DC of 0.796 was obtained. Other techniques involve using Convolutional Neural Networks (CNN) to segment patches taken from the image. For example, [PERSON] et al. [20] proposed a 2D Convolutional Neural Network (2D-CNN) for ischemic stroke segmentation in multimodal MRI images. Their model utilizes a Leaky ReLU activation function. The images were split into patches of size 33 \(\times\) 33 to select portions of the lesion area and healthy brain tissue in a balanced manner to avoid class imbalance. This method achieved a mean DC of 0.7156. Likewise, [PERSON] [21] presented a 3D Convolutional Neural Network with 11 layers for segmenting brain lesions in multimodal MRI scans, which was further studied afterward in a posterior work [22]. The network can process multimodal 3D patches at multiple scales. The system was trained using SGD and achieved an average Dice coefficient of 0.64 before applying preprocessing methods. Another 3D approach is proposed by [PERSON] et al. [23]. They introduced an automatic method to segment acute ischemic stroke from DWI images using a deep 3D fully convolutional and densely connected convolution neural network (3D FC-DenseNet). This method can efficiently utilize 3D contextual information from the MRI slices. The authors tested the performance of the Dice loss and Cross-Entropy loss in the training phase of the model due to the natural class imbalance in this segmentation task. They concluded that the Dice loss led to higher results. Although their model could not segment some brain lesions, the overall DC obtained in the ISLES 2015 dataset was 0.58. The 3D approach helps contextualize the brain lesions through the MRI slices. However, other studies use different views of the MRI sequences to obtain that context. [PERSON] et al. [24] proposed a method whose first stage consists of a Detection and Segmentation Network (DSN) trained with the Focal loss function that segments the lesions from DWI sequences' axial, coronal and sagittal planes. Then, a Multiplane Fusion Network (MPFN) trained with the Dice loss aims to make the prediction more accurate considering the data provided by the DSN. The MPFN reached a DC of 0.622 and a sensitivity of 0.7322. Another dataset employed in state-of-the-art models to test their performance is the ISLES 2022 challenge edition, which presents high-variance scenarios. Different works proposed architectures similar to the previously mentioned. [PERSON] et al. [25] employed Unet-based architectures along with a Boundary Deformation Module (BDM) to enhance the segmentation of the edges of the lesion. Furthermore, the refinement of the edge segmentation is performed by using a multitask learning loss function that combines the Dice loss and Boundary loss functions.