# A Survey on Visual Mamba
## 4 Visual Mamba in Application Fields
### Medical Visual Mamba
#### 4.2.2 Three-Dimensional Medical Images  
Three-dimensional image analysis in medical imaging enables more accurate and comprehensive diagnoses by providing a detailed view of complex anatomical structures. [PERSON] et al. presented nmMamba [70], an innovative architecture designed for 3D medical imaging applications, which integrates local and global relationship modeling via the MICCSS (Mamba-In-Convolution with Channel-Spatial Siamese input) module. nmMamba was tested on a comprehensive benchmark of six datasets for three crucial tasks, including segmentation, classification, and landmark detection, showcasing its capability for long-range relationship modeling at channel and spatial levels.  
Precise 3D segmentation results can alleviate physicians' diagnostic workloads in disease management. SegMamba [71], a cutting-edge architecture, is the first technique to use Mamba expressly for precise 3D medical imaging segmentation. It introduced a tri-orientated Mamba (ToM) module for modeling 3D features from three directions and a gated spatial convolution (GSC) module to enhance spatial feature representation before each ToM module. By similarly employing a U-shaped architecture, LightM-UNet [86] uses the residual vision Mamba layer alone in a Mamba-only method to model large-scale spatial dependencies and extract deep semantic features in a lightweight framework. Both LMa-UNet [45] and T-Mamba [77] built upon the foundation of SegMamba, with improvements made to the Mamba block. A notable aspect of T-Mamba [77] was creating a gate selection unit to adaptively combine two features in the spatial domain with one feature in the frequency domain, whereas LMa-UNet [45] refers to its use of large windows, which outperformed small kernel-based CNNs and small window-based Transformers in local spatial modeling. This marks the first instance of incorporating frequency-based features into the vision Mamba framework. The issue of long-term temporal dependency in video scenarios was also addressed by developing a general framework called Vivim [35], built on Mamba for video vision. Based on a specifically engineered temporal Mamba block, this model effectively compresses long-term spatiotemporal data into sequences of different scales.  
In terms of image registration tasks, MambaMorph [87] introduced a groundbreaking multi-modality deformable registration framework that enhances medical image analysis by combining a Mamba-based registration module with an advanced feature extractor for efficient spatial correspondence and feature learning. The VMambaMorph [83] model further enhanced its VMamba-based block by incorporating a 2D cross-scan module, redesigned to process 3D volumetric features in an efficient way.  
In other domains, the Contrastive Masked Vim Autoencoder (CMViM) [82] tackles Alzheimer's disease (AD) classification by incorporating vision Mamba (ViM) into a masked autoencoder for 3D multi-modal data reconstruction. Regarding endoscopy skill evaluation, a low-cost motion-guided dual-camera tracker [34] provided reliable endoscope tip feedback, and a Mamba-based motion-guided prediction head (MMH) merged visual tracking with historical motion data based on a SSM.