Robust tumor segmentation in incomplete multi-modal imaging via a synergy of diffusion and Mamba models

作者：Ziwei Zou, Xiaoyan Kui, Wenqi Lu, Yang Li, Zhipeng Hu, Jinming Duan, Beiji Zou · 发表于：Biomedical Signal Processing and Control · 年份：2025 · DOI：10.1016/j.bspc.2025.108685 · 被引用次数：2 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Medical Imaging Techniques and Applications、Medical Image Segmentation Techniques

Automatic tumor segmentation is a critical task in medical image analysis. Positron emission tomography (PET) and computed tomography (CT) are widely used in early cancer diagnosis because they provide complementary imaging information about anatomical structures. However, obtaining complete images from both modalities in clinical practice is often challenging due to constraints such as cost and physical limitations. Missing modality data can hinder multi-modal understanding and degrade the performance of automatic tumor segmentation models. Existing methods struggle to effectively exploit cross-modal correlations and capture essential semantic information. To address these challenges, we propose DMM-Net, an end-to-end Diffusion Mamba Multi-modal Network for incomplete multi-modal automatic tumor segmentation. DMM-Net optimizes performance by combining generation and segmentation in a synergistic manner. Specifically, DMM-Net consists of two key components:conditional missing modality generation and cross-modal spatial-channel interaction segmentation. In the first component, a score-based diffusion model generates the missing modality, leveraging the available modalities as conditional guidance to reduce semantic ambiguities. In the second component, we propose a cross-modal interaction Mamba and a cross-modal channel attention enhancement module. The interaction Mamba efficiently captures global contextual features from PET and CT images, facilitating cross-modal feature in...