# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
###### Abstract
Analyzing the area of infection or the entire lung requires a high level of focus because CT-scan images are grayscale, and the radiological characteristics, particularly infections, are often indistinct and difficult to detect. To assist medical personnel, particularly radiologists, in analyzing these subtle and unclear images, we utilize machine learning techniques, specifically image segmentation. This approach enables us to identify lung and infection areas on CT-scans, reconstructing 3D projections that provide a more focused view of the regions of interest. This reconstruction allows medical professionals to gain insights into the precise locations and extents of lung structures and infections. By visualizing the 3D projections, radiologists can compare the size and spatial relationships between the lung and infection areas, aiding in more accurate diagnosis and treatment planning. Looking forward, this method also holds the potential for calculating the volume of specific areas, such as the volume of the infection, further enhancing the quantitative analysis capabilities for medical practitioners. Image segmentation is one of the most critical tasks in medical image analysis, involving the recognition and separation of various components in an image [8]. With the dynamic strides in machine learning, particularly in the revolutionary domain of deep learning, substantial headway has been achieved in this field. Among these advancements, the U-shaped deep learning architectures, originally stemming from UNet [9], have risen to prominence as prevalent frameworks for image segmentation, unequivocally showcasing their progress across diverse medical imaging scenarios. In our research, we will harness the power of multiple U-shaped deep learning architectures. This inclusive lineup encompasses its precursor, UNet, along with its subsequent developments: LinkNet [10], Attention UNet [11], UNet 3+ [12], and TransUNet [13]. We will utilize these architectures, specifically targeting the identification and separation of the lung and infection areas, using data obtained from COVID-19 lung patients. Our aim is to evaluate and compare the performance and efficacy of these five 2D U-shaped deep learning architectures in generating accurate 3D lung reconstructions within this specific context. UNet, LinkNet, Attention UNet, UNet 3+, and TransUNet stand as prominent deep learning models extensively employed within the domain of medical image analysis, particularly in the intricate realm of semantic segmentation tasks. These versatile modelshave demonstrated remarkable efficacy across a diverse spectrum of medical imaging applications, including image segmentation in CT-scans, MRI scans, and microscopy images [9; 11; 12; 13; 14; 15; 16; 17; 18]. In this paper, we focus on reconstructing the 3D projection of lung CT-scans using 2D deep learning architectures, considering the advantages they offer. While 3D models are possible for this task, employing 2D architectures provides computational efficiency during training and inference. By leveraging 2D models, we can achieve comparable results to 3D models, albeit with the need for additional post-processing steps to convert the 2D output to a 3D projection, unlike 3D models, which are more straightforward and do not require any post-processing steps [14]. This approach strikes a balance between computational efficiency and accurate visualization, which is crucial for CT-scan analysis. Moreover, using 2D architectures allows us to capture both local and global context while preserving high-resolution features. This methodology enables faster analysis and interpretation of lung CT-scans, providing valuable insights for medical personnel. In summary, we contribute to the domain of medical imaging by leveraging a comprehensive array of advanced deep learning techniques for the paramount task of 3D projection reconstruction from 2D lung CT-scans.