VMDUnet: Advancing Glioma Segmentation Integrating With Mamba and Dual Cross-Attention

Gliomas are the most common type of primary brain tumor, characterized by their diffuse invasiveness and origin within the central nervous system. Manual identification and segmentation of tumor regions in MRI is a time-consuming and subjective process, and may negatively impact diagnostic accuracy because the heterogeneity and infiltrative pattern of glioma are complex. To address these problems, we propose an automated glioma segmentation approach named IADSG (Intelligent Assistant Diagnosis System for Glioma), based on our novel VMDUnet architecture. Our method incorporates Contrast Limited Adaptive Histogram Equalization (CLAHE) as a preprocessing step to enhance image contrast and quality. Moreover, we use data augmentation techniques to improve the generalization and adaptability to complex clinical images of the model. Crucially, the integration of a Mamba module and a dual cross-attention mechanism enables the model to effectively balance segmentation accuracy with computational efficiency. Experimental results show that our approach achieves a segmentation accuracy of 0.7769 DSC on the internal glioma dataset and 0.9117 DSC on the public BraTS dataset, outperforming existing segmentation methods on both benchmarks. This approach reduces the time and effort involved in manual segmentation, reduces the probabilities of misdiagnosis, and provides robust support for the diagnosis and treatment to be accurately conducted. Our code is available at https://github.com/CarioAo/VMDUnet.