# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 2 Related Works
The groundbreaking performance of Attention UNet has catalyzed the development of several derivative models, including Attention-augmented UNet (AA-UNet) [32], Multi-Scale Dilated Attention UNet (MDA-Unet) [33], and much more, as researchers continue to draw inspiration from its proven efficacy. UNet 3+ [12], a model derived from the UNet++ architecture, stands as another integral U-shaped framework that holds significance in this study and has garnered widespread attention. UNet 3+ is designed to harness the full potential of multi-scale features extracted across various levels of both encoder and decoder pathways. Its debut saw applicationson two distinct datasets: the liver segmentation dataset sourced from the ISBI LiTS 2017 challenge and the spleen dataset. Remarkably, in comparison with five state-of-the-art methods, including PSPNet [34], Deeplab variations [35; 36; 37], and Attention UNet [11], UNet 3+ showcased its supremacy on both datasets. Employing a hybrid loss with Conditional Gated Module (CGM), UNet 3+ achieved the highest performance, attaining remarkable evaluation scores of 96.75% and 96.20% for the liver and spleen datasets, respectively. The prowess of UNet 3+ extends beyond its initial applications. Widely embraced, this model has found utility in various studies, not only as a primary method but also as a robust benchmark in various studies. TransUNet [13], a pivotal advancement within the landscape of image segmentation, marks the fusion of two potent domains, the Transformer architecture and UNet. Notably, TransUNet introduces self-attention mechanisms to meticulously capture the global context from input data, setting the stage for its successful application in medical image segmentation. This remarkable model embarks on the segmentation journey across two diverse medical datasets: the Synapse dataset, encompassing various structures, and the ACDC dataset, focused on cardiac MRI scans. In the scrutiny of the Synapse dataset, TransUNet takes on established contenders including VNet [38], DARR [39], UNet (with ResNet50 as the encoder), Attention UNet (with ResNet50 as the encoder) [40], and various ViT variations. Impressively, TransUNet emerges triumphant, securing the highest dice score of 77.48%. Similarly, in the context of the ACDC dataset, TransUNet goes head-to-head with UNet (with ResNet50 as the encoder), Attention UNet (with ResNet50 as the encoder), and assorted ViT [41] variations, once again clinching the top spot with a remarkable dice score of 89.71%. The profound influence of TransUNet reverberates throughout the medical imaging community, culminating in the emergence of derivative architectures such as Multilevel TransUNet (MTUNet) [42], MT-TransUNet [43], TransUNet++ [44], and a host of other innovative creations, solidifying TransUNet as a cornerstone and blueprint for future developments. Previous studies have demonstrated the widespread utilization of UNet, LinkNet, Attention UNet, UNET 3+, and TransUNet architectures for achieving high accuracy results in semantic segmentation tasks. This study aims to address the analysis of these five U-shaped architectures, specifically evaluating their performance and effectiveness, while also exploring the advantages and limitations of employing 2D architectures for generating 3D projections.