Methods and Challenges of U-Net in CT Segmentation of Pulmonary Micronodules

Pulmonary micronodules often present with weak contrast, blurred boundaries, and easy adhesion to blood vessels or pleura in Computed Tomography (CT), which leads to missed detection and missegmentation in automatic segmentation, affecting the consistency of screening and follow-up evaluation. This paper reviews the research progress of U-Net and its improved structures in the segmentation of pulmonary micronodules. It focuses on summarizing technical routes such as multi-scale feature fusion, attention mechanism, three-dimensional convolution, and convolutional Transformer hybrid structure, and summarizes and compares the performance of common indicators of related methods on datasets such as DCC-IDRI and LUNA16. Overall, multi-scale dense connection, spatial-channel attention, and long-range dependency modeling are helpful to improve the stability of edge characterization and segmentation of small nodules, but they are still constrained by factors such as weak features, background complexity, and cross-device domain offset. In the future, the robustness and generalization ability in multi-center scenarios can be further enhanced from directions such as lightweight 3D modeling, multi-modal fusion, semi-supervised learning, and explainability mechanisms.