# A Survey on Visual Mamba
## 4 Visual Mamba in Application Fields
### Medical Visual Mamba
#### 4.2.1 Two-Dimensional Medical Images  
Mamba has exhibited impressive potential in 2D medical segmentation, as displayed in Figure 6. Here, we discuss in detail some methods that explore the use of mamba to model the global structure information of 2D medical segmentation. Most of the innovative architectures that have been developed are based on U-Net, it has shown outstanding results in a variety of medical image segmentation challenges. U-Lambda [84] refers to the first extension of the mamba model to the U-Net framework for visual segmentation in biomedical imaging, addressing the challenge of long-range depen \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Sub-Category** & **Method** & **Efficiency** & **Code** \\ \hline \multirow{8}{*}{Segmentation} & & LMa-UNet [45] & - & ✓ \\ \cline{2-5}  & & LightM-UNet [86] & Params 1.87 & ✓ \\ \cline{2-5}  & & FLOPs 457.62 \(\times\) 10\({}^{9}\) & \\ \cline{2-5}  & & SegMamba [71] & Inference speed 151 & ✓ \\ \cline{2-5}  & & T-Mamba [77] & - & ✓ \\ \cline{2-5}  & & Vivim [35] & FPS 35.33 & ✓ \\ \cline{2-5}  & & Classification & CMVIM [82] & Params 50 & ✓ \\ \cline{2-5}  & & Motion tracking & Motion-Guided Dual-Camera & - & ✓ \\ \cline{2-5}  & & Tracker [46] & & \\ \cline{2-5}  & & Backbone & nnMamba [70] & Params 15.55 & ✗ \\ \cline{2-5}  & & & FLOPs 141.14 & \\ \cline{2-5}  & & & Inference speed 19 & \\ \cline{2-5}  & & VMambaMorph [53] & Memory 3.93 & ✓ \\ \cline{2-5}  & & Params 9.64 & & \\ \cline{2-5}  & & & Inference speed 27 & \\ \cline{2-5}  & & Memory 7.60 & ✓ \\ \cline{2-5}  & & Params 7.59 & & \\ \hline \hline \end{tabular} For efficiency, inference speed is in ms, memory is in Gb, Params is in M, and FLOPS is in G.  
\end{table} 
Table 8: _Cont._  
Figure 6: An overview of Mamba models used for segmentation task in 2D medical images. dency modeling, which is captured by a hybrid CNNs-SSM block. [PERSON] et al. introduced the High-order Vision Mamba UNet (H-vmunet) [55], an improvement on U-Mamba, which employs a high-order 2D-selective scan at each interaction to bolster the learning of local features, while minimizing the incorporation of redundant information. Shortly after their initial release, the team expanded their work by introducing the UltraLight VM-UNet [62]. This new iteration was developed through in-depth analysis of the critical factors affecting parameter efficiency within the Mamba framework. This resulted in a significantly more lightweight model with a mere 0.049M parameters and a computational efficiency of only 0.060 GFLOPs. Moreover, Mamba-UNet [51] combines the encoder-decoder architecture of U-Net with the capabilities of Mamba and maintains spatial information at different network scales based on jump connections. A Visual Mamba-based VSS block is used, which utilizes linear embedding layers and deep convolution to extract features, while downsampling and upsampling are facilitated by multiple merge operations and extension layers for comprehensive feature learning. Both Pyramid ViT (PVT) and Swin-Unet are pioneering hierarchical designs that apply visual tasks and propose progressive shrinking pyramids and spatial-reduction attention. By drawing inspiration from PVT and Swin-Unet, [PERSON] et al. introduced VM-UNet [63], a foundational model for purely SSM-based segmentation in medical imaging. This model shows the capabilities of SSMs in medical image segmentation and is made up of three primary components: an encoder, a decoder, and skip connections. By building on their previous work, the team proposed VM-UNet-V2 [64]. The purpose of the visual state space (VSS) block was to obtain a greater range of contextual information. A semantics and detail infusion (SDI) mechanism was also implemented to enhance the fusion of low-level and high-level features. Mamba-HUNet [59], as another multi-scale hierarchical upsampling network, incorporates the Mamba technique.