3D-DDN Faster R-CNN: Pulmonary Nodule Detection Through Cross-Slice Feature Fusion and Dynamic Displacement Modeling

Accurate pulmonary nodule detection in computed tomography (CT) remains challenging because small- and low-contrast nodules may resemble vessels and other anatomical structures on individual axial slices. We propose 3D-DDN Faster R-CNN, a multi-slice detection framework that incorporates structured local axial context into a two-stage detector. The five-slice interactive blending (5-SIB) strategy organizes five consecutive CT slices as an ordered contextual group and preserves slice-specific feature streams extracted by a shared two-dimensional backbone. The cross-slice feature fusion (CSFF) module applies slice-wise spatial self-attention at shallow feature levels and learnable cross-slice aggregation at deep feature and region-of-interest levels. The dynamic displacement constraint (DDC) models proposal displacement across adjacent slices using a positive-sample regression loss and a margin-based negative-sample contrastive loss, while a class-weighted focal loss alleviates foreground-background imbalance. The model was evaluated on a private hospital dataset, LIDC-IDRI, and LNDb using patient-level data partitions. On the private hospital dataset, 3D-DDN achieved an average precision at an IoU threshold of 0.5 (AP_0.5) of 75.80% ± 1.05%, a sensitivity of 89.33% ± 1.91%, and a competition performance metric (CPM) of 91.20% ± 1.50%. The corresponding AP_0.5 values on LIDC-IDRI and LNDb were 66.27% ± 0.45% and 48.20% ± 0.30%, respectively. Ablation experiments showed that direct five-slice stacking degraded performance, whereas structured feature fusion and displacement supervision recovered the performance loss and improved the overall detection profile. These results indicate that ordered cross-slice representation, hierarchical feature fusion, and proposal-displacement supervision provide complementary mechanisms for incorporating local three-dimensional context.