Context-Gated Cross-Modal Perception with Visual Mamba for PET-CT Lung Tumor Segmentation

TL;DR

This paper introduces vMambaX, a lightweight multimodal framework that uses a context-gated perception module to improve lung tumor segmentation by effectively integrating PET and CT images, outperforming baselines.

Contribution

The study presents vMambaX, a novel adaptive cross-modal gating framework based on Visual Mamba, enhancing multimodal feature interaction for lung tumor segmentation.

Findings

Outperforms baseline models on PCLT20K dataset

Maintains lower computational complexity

Effective adaptive cross-modal gating enhances segmentation

Abstract

Accurate lung tumor segmentation is vital for improving diagnosis and treatment planning, and effectively combining anatomical and functional information from PET and CT remains a major challenge. In this study, we propose vMambaX, a lightweight multimodal framework integrating PET and CT scan images through a Context-Gated Cross-Modal Perception Module (CGM). Built on the Visual Mamba architecture, vMambaX adaptively enhances inter-modality feature interaction, emphasizing informative regions while suppressing noise. Evaluated on the PCLT20K dataset, the model outperforms baseline models while maintaining lower computational complexity. These results highlight the effectiveness of adaptive cross-modal gating for multimodal tumor segmentation and demonstrate the potential of vMambaX as an efficient and scalable framework for advanced lung cancer analysis. The code is available at https://github.com/arco-group/vMambaX.