MambaSeg: Scalable SSM for Segmentation

• MambaSeg is a family of neural segmentation architectures that leverages state-space models (Mamba S6) as a scalable alternative to traditional convolution and self-attention.
• It employs linear-time selective scanning and multi-directional, multi-scale processing within U-Net style encoder–decoder designs for enhanced global context integration.
• MambaSeg delivers high-resolution segmentation performance across diverse domains including medical imaging, remote sensing, and crack detection, often surpassing transformer-based methods.

MambaSeg refers to a family of neural segmentation architectures that employ selective State Space Models (SSMs), particularly the Mamba (S6) model, as an alternative to self-attention and convolution for structured, efficient, and scalable long-range dependency modeling. MambaSeg architectures have been adopted across domains including medical imaging (2D and 3D), remote sensing, crack detection, and multimodal event-based perception. Their distinguishing attributes include linear complexity, hardware-aware selective scanning, compatibility with U-Net style encoder–decoders, and performance on par with or surpassing transformer-based approaches in high-resolution, long-context image analysis (Bansal et al., 2024, Gu et al., 30 Dec 2025, Lumetti et al., 2024, Wang et al., 25 Mar 2025, Yang et al., 13 Jan 2025, Nguyen et al., 2024, Bui et al., 4 Oct 2025, Liu et al., 3 Mar 2025).

1. Foundations: State-Space Models and the Mamba Layer

At the core of MambaSeg is the SSM, which models sequential dependencies via the recurrence: $$h_{t+1} = \overline{A} h_t + \overline{B} x_t,\quad y_t = C h_t,$$ where input $x_t$, hidden state $h_t$, and output $y_t$ are updated using trainable operators $(A, B, C)$ discretized via zero-order hold or bilinear transforms. In the Mamba (S6) variant, $B$, $C$, and time step $\Delta$ are made data-adaptive, and efficient "selective scan" operators enable linear-time ($O(N)$) propagation for arbitrarily long sequences (Bansal et al., 2024, Lumetti et al., 2024).

Vision Mamba adapts this framework for images and 3D volumes by flattening spatial grids into long 1D or multi-directional sequences, wrapping the scan with layer normalization, MLP projections, depthwise convolution, and nonlinearity (e.g., SiLU). Bidirectional and multi-axial scanning further enhance context integration. For volumetric segmentation, MambaSeg replaces or augments convolutional and transformer backbones with Mamba layers to provide global receptive fields while retaining local resolution (Lumetti et al., 2024, Wang et al., 25 Mar 2025).

2. Architectural Variants and Network Designs

MambaSeg instantiates as pure Mamba U-Nets, hybrid Mamba-convolutional/transformer networks, and multimodal or structure-aware pipelines. The following table summarizes representative variants and domains:

Variant / Paper	Backbone Modality	Salient Innovations
MambaSeg (Gu et al., 30 Dec 2025)	Event+RGB Dual VSS (VMamba-T)	DDIM (CSIM+CTIM) for cross-modal fusion
UlikeMamba_3dMT (Wang et al., 25 Mar 2025)	3D U-Net, Mamba+3D DWConv	Multi-scale/tri-scan SSM, MSv4, Tri-scan
MSV-Mamba (Yang et al., 13 Jan 2025)	2D U-Net, LMS decoder	Local+global BiMamba, MSAA, Aux. losses
AC-MambaSeg (Nguyen et al., 2024)	U-Net, ResVSS (CNN+SS2D)	CBAM, AG, Selective Kernel Bottleneck
SCSegamba (Liu et al., 3 Mar 2025)	Vision Mamba+GBC	Structure-aware scan+gating for cracks
MambaCAFU (Bui et al., 4 Oct 2025)	CNN+Transformer+Mamba encoder	MAF, CoAG, multi-scale fusion

This diversity reflects the adaptability of Mamba layers as replacements or complements to convolution and attention across domains and data types (Bansal et al., 2024).

3. Dual-Branch and Multimodal Fusion Approaches

In multimodal segmentation, such as fusing event and frame data, MambaSeg architectures employ dual-branch encoders with parallel VSS or VMamba backbones for each modality (e.g., RGB images and event voxel grids). The Dual-Dimensional Interaction Module (DDIM), comprising Cross-Spatial (CSIM) and Cross-Temporal (CTIM) Interaction Modules, enables exchange between modalities at each scale.

• CSIM performs shallow fusion, spatial pooling, modality-specific attention, and bidirectional 2D SSM refinement, followed by spatial attention-based residual updates.
• CTIM interleaves modalities temporally, applies global temporal pooling, attention, bi-directional scan, and temporal-attention residual updating.

Alternating CSIM and CTIM at each stage progressively aligns both spatial and temporal cues between modalities, crucial for robust perception under challenging conditions (e.g., rapid motion, low light) (Gu et al., 30 Dec 2025).

4. Multi-Scale and Multi-Directional Extensions

To maximize spatial context coverage and resolve details in large 3D or high-res 2D images, MambaSeg incorporates advanced multi-scale and directional scanning strategies.

In volumetric medical segmentation (Wang et al., 25 Mar 2025, Lumetti et al., 2024):

• Multi-scale Mamba blocks (MSv1–MSv4) integrate parallel or concatenated 3D convolutions with various receptive fields, feeding their outputs into Mamba SSMs before projection and residual addition. MSv4 in particular (three DWConv3D branches + SSM) provides strong Dice gains with moderate parameter cost.
• Tri-scan architecture (axis-aligned sequential flattening and SSM along $x$, $y$, $z$) enhances context integration relative to single or dual-scan approaches, especially in multi-organ and complex anatomical tasks.
• Skip-path SSMs further refine high-resolution decoder features.

Ablation studies consistently demonstrate that tri-scan and multi-scale blocks deliver up to +1.1 Dice over standard 1D scan baselines, with manageable computational overhead (Wang et al., 25 Mar 2025).

For fine-structure segmentation (e.g., cracks, tubular structures), multi-directional SASS scanning (multiple interleaved snakes) and Gated Bottleneck Convolutions target topological continuity and dynamic background suppression with minimal parameters (Liu et al., 3 Mar 2025).

5. Training Objectives, Optimization and Implementation

MambaSeg implementations follow U-Net-style encoder–decoder patterns, often with deep supervision via auxiliary losses, spatial/channel attention, and adaptive bottleneck fusion. Losses combine region-based metrics (Dice, Tversky) and pixelwise cross-entropy, with class-imbalance weighting or focal loss as needed.

Typical hyperparameters:

• Adam/AdamW or RAdam optimizer
• Initial LR $=10^{-4}$ to $3\times 10^{-4}$, cosine or plateau decay
• Data augmentation: flips, rotations, intensity jitter, elastic deformations, patch cropping
• Deep supervision: auxiliary cross-entropy at each decoder stage (Yang et al., 13 Jan 2025, Nguyen et al., 2024, Lumetti et al., 2024)
• Efficient training via hardware-aware scan implementations (SRAM) and residual scaling to stabilize deep SSMs (Lumetti et al., 2024)

Pretraining on large vision corpora (e.g., ImageNet-1K) and self-supervised proxy tasks (masked interpolation, contrastive learning) are widely adopted in medical and multimodal variants (Bansal et al., 2024).

6. Quantitative Benchmarks and Empirical Performance

Across a wide spectrum of segmentation benchmarks, MambaSeg architectures consistently achieve or exceed state-of-the-art accuracy with lower parameter and compute budgets. Selected results include:

Dataset/Domain	Model/Variant	Score(s)	Params/FLOPs	Ref
3D-medical (AMOS)	UlikeMamba_3dMT, MSv4	Dice 89.95	31.6M / 62.2G	(Wang et al., 25 Mar 2025)
Multimodal (DDD17)	MambaSeg (DDIM)	mIoU 77.56%, Acc. 96.33%	25.4M / 15.6G	(Gu et al., 30 Dec 2025)
Echocardio (CAMUS)	MSV-Mamba (Full)	LV_endo 95.01, LV_epi 87.35	--	(Yang et al., 13 Jan 2025)
Skin lesion (ISIC)	AC-MambaSeg	DSC 0.9068, IoU 0.8417	8.0M / 2.1G	(Nguyen et al., 2024)
Crack (TUT)	SCSegamba	F1 0.8390, mIoU 0.8479	2.8M / 18.2G	(Liu et al., 3 Mar 2025)
Multi-organ Abdomen	MambaCAFU	DSC 84.87%, HD95 17.15	66.7M / 40.3G	(Bui et al., 4 Oct 2025)

Ablation studies confirm that Mamba layers, multi-directional/scale scanning, structure-aware gating, and multimodal interaction modules each provide substantial incremental gains. In high-complexity scenarios (TotalSegmentator, >50 labels), tri-scan and multi-scale MSv4 architectures yield the best accuracy-to-compute trade-off (Wang et al., 25 Mar 2025). In light-resource or embedded applications, lightweight variants (e.g., SCSegamba, LightM-UNet) deliver strong accuracy with minimal parameter counts (Liu et al., 3 Mar 2025, Bansal et al., 2024).

7. Limitations, Challenges, and Future Research Directions

While MambaSeg provides an efficient, hardware-adaptive foundation for high-resolution segmentation, several challenges remain:

• Loss of spatial geometry due to input flattening, addressed partially by hybrid convolutional-SSM designs and advanced scan orders (Bansal et al., 2024).
• Theoretical interpretation of SSM generalization and optimal scan strategies remains incomplete; the impact of large SSM blocks on stability requires further study (Bansal et al., 2024, Wang et al., 25 Mar 2025).
• Full non-causal 2D/3D scanning is an open research problem.
• Pretraining and transfer strategies for SSM layers (“Mamba foundation models”) on large-scale medical or multimodal corpora are emergent.
• New directions include integrating state-space duality (Mamba 2), omnidirectional scan paths, and xLSTM-like matrix-state recurrences (Bansal et al., 2024).

A plausible implication is that structured, learnable state-space modeling will continue to supplant both quadratic attention and local convolution in high-resolution semantic segmentation pipelines. Current and future work on multimodal, few-shot, and generalist SSM–segmentation architectures is expected to expand the versatility and accuracy of MambaSeg in real-world applications.