M-AECA Net: A Mamba-Based Auxiliary Encoder with Cross-Attention Fusion Network for PET/CT Tumor Segmentation

The combination of positron emission tomography (PET) and computed tomography (CT) can accurately reflect the metabolic and anatomical information of a variety of tumors, including nasopharyngeal carcinoma, lymphoma and lung cancer, which plays an important role in the diagnosis, staging and efficacy evaluation of tumors. Accurate and automatic delineation of target tumors is crucial for radiotherapy, however, the tumor segmentation task is extremely challenging due to the fuzzy tumor boundaries, uncertain locations, and the scattered distribution of multiple tumors throughout the body. To this end, this study extended the STUNet pre-trained on the TotalSegmentator dataset and proposed M-AECA, which integrates a Mamba-based auxiliary encoder (M-AE) to provide multi-scale global features for enhanced feature extraction. In addition, an Inter-Branch Feature Fusion Module (IBFFM) is designed to achieve more comprehensive global and local feature fusion through cross attention (CA) and feature subspace projection. The method was evaluated on Hecktor and AutoPET datasets, demonstrating superior performance compared to other comparison methods. In the test sets of these two datasets, the average Dice similarity coefficients of the proposed method were 70.86% and 64.91%, respectively. In addition, the results of the ablation experiment show that the proposed M-AE and IBFFM demonstrate strong performance and significant advantages.