Cross-Graph Interaction and Diffusion Probability Models for Lung Nodule Segmentation

Accurate segmentation of lung nodules in computed tomography (CT) images is crucial to advance the treatment of lung cancer. Methods based on diffusion probabilistic models (DPMs) are widely used in medical image segmentation tasks. Nevertheless, conventional DPM encounters challenges when addressing medical image segmentation issues, primarily attributed to the irregular structure of lung nodules and the inherent resemblance between lung nodules and their surrounding environments. Consequently, this study introduces an innovative architecture known as the dual-branch Diff-UNet to address the challenges associated with lung nodule segmentation effectively. Specifically, the denoising UNet in this architecture interactively processes the semantic information captured by the branches of the Transformer and the convolutional neural network (CNN) through bidirectional connection units. Furthermore, the feature fusion module (FFM) helps integrate the semantic features extracted by DPM with the locally detailed features captured by the segmentation network. Simultaneously, a lightweight cross-graph interaction (CGI) module is introduced in the decoder, which uses region and edge features as graph nodes to update and propagate cross-domain features and capture the characteristics of object boundaries. Finally, the multi-scale cross module (MCM) synergizes the deep features from the DPM with the edge features from the segmentation network, augmenting the network’s capability to comprehend images. The Diff-UNet has been proven effective through experiments on challenging datasets, including self-collected datasets and LUNA16.