An Enhanced U-Net Model for Segmenting CT Images of COVID-19 Patients

In the context of the global COVID-19 pandemic, medical imaging technology has played a crucial role in the diagnosis and treatment of the disease. Accurate segmentation of lesion areas in CT images is critical for assessing the condition and formulating treatment plans. This paper first outlines the importance of medical image segmentation. It then delves into the main challenges faced in image segmentation for COVID-19 diagnosis, including the diversity of lesions, inconsistencies in image quality, and the need for real-time processing. Following this discussion, the paper reviews existing medical image segmentation models, encompassing traditional methods such as watershed and threshold segmentation, as well as advanced deep learning models like U-Net and RCNN. Building on this foundation, the paper proposes an improved image segmentation framework aimed at enhancing the accuracy and processing speed of lesion area segmentation. The goal is to provide a more reliable decision support tool for clinical practice.