# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 2 Related Works  
Image segmentation has emerged as a significant research domain in recent times, with the application of various deep learning techniques for this task. Among the numerous deep learning techniques for image segmentation, the U-shaped architectures have gained a lot of attention due to their efficacy in performing segmentation on medical images. This section presents works related to our research that utilized the UNet, LinkNet, Attention UNet, UNET 3+, and TransUNet architectures for image segmentation. The authors in [19] presented four different semantic segmentation networks based on the UNet architecture with different backbones, namely ResNet50-UNet, DenseNet169-UNet, SE-ResNet50-UNet, and EfficientNetB4-UNet, which were employed for the segmentation of chest X-ray images to diagnose Pneumothorax. The evaluation of these models achieved an IoU score of around 78% and a mean dice similarity coefficient (DSC) score of 90%. [PERSON] et al. [20], modified the UNet architecture, substituting the convolutions layer with residual blocks and developed an architecture called ResUNet. A deep residual UNet was used to segment liver tumor CT-scans and obtained a DSC score of 89.3%, an accuracy of 97%, a precision of 95%, and a specificity 95.7%. [PERSON] et al. [14] proposed various different encoder architectures for 3D UNet for segmenting the COVID-19 CT Lung and Infection Dataset. The evaluated architectures include 3D UNet, 3D ResUNet, 3D VGGUNet, and 3D DenseUNet. The 3D UNet architecture demonstrated high accuracy, producing an IoU score of 81.58% and a dice score of 88.61% for lung and infection segmentation. The LinkNet algorithm was used in [21] to develop a Multi-Task network on LinkNet-based architecture (MTLN) with multi-scale inputs. The architecture achieves a DSC score of 96.84% by performing semantic segmentation and estimating fetal head circumference (HC) in 2D ultrasound images. [PERSON] et al. [22] presented a new approach to the LinkNet-based algorithm using EfficientNetB7 as the encoder, called LinkNet-B7, for noise removal and segmentation of skin cancer images. The proposed model obtained a dice coefficient of 95.72% and 96.70%, respectively. The work presented in [23] proposes a different approach for the LinkNet-based algorithm, where LinkNet is used with a pre-trained encoder as its backbone, and additional dilated convolution layers are added in the center part of the network. Attention UNet [11], a model that introduces attention gates, has made a considerable impact and served as an inspiration for numerous researchers in the realm of image segmentation. Initially proposed to address segmentation challenges in medical contexts, Attention UNet has significantly advanced the field by harnessing attention mechanisms. In its seminal work, the application of Attention UNet was demonstrated on abdominal CT-scans, specifically the multi-class abdominal CT-150, as well as the TCIA Pancreas CT-82 dataset [24]. Notably, Attention UNet consistently outperformed UNet, excelling particularly in the demanding task of pancreas segmentation. In the same study, Attention UNet exhibited its prowess against benchmark models on the CT-82 dataset, including hierarchical 3D FCN [25], dense-dilated FCN [26], holistically nested 2D FCN stage-1 and stage-2 [27], 2D FCN [28], 2D FCN + recurrent Networks [28], and single and multi-model 2D FCN [29]. It achieved comparable performance despite using lower resolution and omitting post-processing steps. The influence of this model extends beyond abdominal CT-scans. [PERSON] et al. [30] employed Attention UNet with Active Contour Based Hybrid Loss for brain tumor segmentation, where it exhibited superiority over SegNet [31] and UNet, achieving dice and IoU scores of 89% and 81%, respectively.