3D Lymphoma Segmentation on PET/CT Images via Multi-Scale Information Fusion with Cross-Attention

Background: Accurate segmentation of diffuse large B-cell lymphoma (DLBCL) lesions is challenging due to their complex patterns in medical imaging. Objective: This study aims to develop a precise segmentation method for DLBCL using 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) images. Methods: We propose a 3D dual-branch encoder segmentation method using shifted window transformers and a Multi-Scale Information Fusion (MSIF) module. To enhance feature integration, the MSIF module performs multi-scale feature fusion using cross-attention mechanisms with a shifted window framework. A gated neural network within the MSIF module dynamically balances the contributions from each modality. The model was optimized using the Dice Similarity Coefficient (DSC) loss function. Additionally, we computed the total metabolic tumor volume (TMTV) and performed statistical analyses. Results: The model was trained and validated on a dataset of 165 DLBCL patients using 5-fold cross-validation, achieving a DSC of 0.7512. Statistical analysis showed a significant improvement over comparative methods (p < 0.05). Additionally, a Pearson correlation coefficient of 0.91 and an R^2 of 0.89 were observed when comparing manual annotations to segmentation results for TMTV measurement. Conclusion: This study presents an effective automatic segmentation method for DLBCL that leverages the complementary strengths of PET and CT imaging. Our method has the potential to improve diagnostic interpretations and assist in treatment planning for DLBCL patients.