High-precision medical image segmentation provides a reliable basis for clinical analysis and diagnosis. Researchers have developed various models to enhance the segmentation performance of medical images. Among these methods, two-dimensional models such as Unet exhibit a simple structure, low computational resource requirements, and strong local feature capture capabilities. However, their spatial information utilization is insufficient, limiting their segmentation accuracy. Three-dimensional models, such as 3D Unet, utilize spatial information more fully and are suitable for complex tasks, but they require high computational resources and have limited real-time performance. In this paper, we propose a virtual 3D module (Mambav3d) based on mamba, which introduces spatial information into 2D segmentation tasks to more fully integrate the 3D information of the image and further improve segmentation accuracy under conditions of low computational resource requirements. Mambav3d leverages the properties of hidden states in the state space model, combined with the shift of visual perspective, to incorporate semantic information between different anatomical planes in different slices of the same 3D sample. The voxel segmentation is converted to pixel segmentation to reduce model training data requirements and model complexity while ensuring that the model integrates 3D information and enhances segmentation accuracy. The model references the information from previous layers when labeling the current layer, thereby facilitating the transfer of semantic information between slice layers and avoiding the high computational cost associated with using structures such as Transformers between layers. We have implemented Mambav3d on Unet and evaluated its performance on the BraTs, Amos, and KiTs datasets, demonstrating superiority over other state-of-the-art methods.
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