In view of the flaws of under-segmentation and missed detection in fine-grained image segmentation tasks, an improved end-to-end 3D segmentation algorithm, RSE-Vnet, has thus been proposed. With Res2net network incorporated, multi-scale fine-grained features of different nodules can be captured, thus feeding more accurate nodule location information to the network. Meanwhile, residual connections help to avoid network degradation issues, thus establishing a data-driven model for nodules. The attention mechanism can effectively weight important feature channels so as to reduce the interference of background images, with the constructed method solving the problem of under-segmentation and missed detection of multiple types of nodules to some extent. Finally, it can be verified in the LUNA16 dataset, with a 7% increase in model DSC and a 6% increase in detection sensitivity specifically.