## 2 Related Work
### Deep Learning-Based Semantic Segmentation  
The VGG [58] and ResNet [59] architectures are amongst the most widely used for feature extraction. VGG-based methods [58; 60] have displayed good segmentation performance and have simple, straightforward structures. However, these models require high computational power during training, as they use a large number of parameters. The latter can also affect their inference time and limit their use in real-time applications.  
Architectures that take the residual block as its focus address the vanishing gradient problem effectively. Models such as ResNet [59], FusionNet [61], and Faster-RCNN [62], amongst others, have shown robust segmentation results. However, it is worth noting that large-scale usage of skip connections can lead to memory problems [53], with the In-Place Activated Batch Normalization (INPLACE-ABN) model [63] seeking to reduce the training memory footprint of ResNet-based architectures. The FRRN architecture [64] proposes a two-stream structure that incorporates elements of both VGG and ResNet approaches.  
The DeepLab architecture [65] follows an approach that focuses on recovering the spatial resolution through the use of atrous convolutions to generate high-resolution feature maps [53]. It is worth noting that methods based on Atrous Spatial Pyramid Pooling (ASPP) modules display a steep computational cost [66]. There have been several expansions on the DeepLab architecture [67; 68]. The most recent one is the DeepLabV3+ [66] model, in which the authors seek to reduce the computational complexity through the application of depth-wise separable convolutions to the ASPP and decoder modules.  
It is worth noting that the VGG, residual block, and atrous convolution-based approaches rely on considerably big datasets for training. Depending on the application context, datasets of sufficient scale may not be available.  
In contrast, the U-Net architecture [69] aims to perform image segmentation on smaller datasets. This architecture consists of convolution and deconvolution layers in an encoder-decoder fashion, where the high-resolution features from the encoder layers are combined with the up-sampled outputs of the decoder's layers. An advantage of this model is its small number of parameters, which allows for fast training and inference times; however, the use of skip connections tends to use redundant information in low-level encoder features, particularly in multi-scale approaches [70]. The Attention U-Net [46] builds upon the U-Net concept through the proposal of Attention Gates (AGs), which learn to focus on specific  
Figure 2: Sample basic architecture of a CNN. The last layers can differ depending on the particular application (e.g., classification, segmentation, etc.).  
structures without additional supervision, suppressing feature activations in irrelevant regions [69]. A more recent approach is the spatial-channel attention gate (scAG) [70] that implements an attention mechanism that emphasizes meaningful information along both the channel and spatial dimensions to overcome the mentioned drawbacks of the U-Net's skip connections. Finally, it is worth noting that most of the U-Net-based models are proposed and evaluated for the particular task of biomedical image segmentation.  
It is worth noting that all the discussed techniques have displayed robust results for semantic segmentation. However, they have different characteristics that may be desirable given a specific application. Table 1 shows a summary of their associated advantages and disadvantages.