DAMamba-UNet3D: A Parameter-Efficient Mamba State Space U-Net with Dynamic Adaptive Scan for 3D Medical Image Segmentation

• URL: http://arxiv.org/abs/2607.22718v1
• Date: Tue, 21 Jul 2026 16:42:48 GMT
• Title: DAMamba-UNet3D: A Parameter-Efficient Mamba State Space U-Net with Dynamic Adaptive Scan for 3D Medical Image Segmentation
• Abstract summary: DAMamba-UNet3D is a hybrid encoder-decoder that integrates tri-plane 3D-DAS blocks at encoder stages E2-E4 while retaining convolutions elsewhere.<n>On BraTS 2020 five-fold cross-validation, DAMamba-UNet3D achieves mean Dice 0.815 +/-0.013 at 13x lower parameter cost than SegMamba.
• Score: 1.9414157088543738
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose parameter-efficient SSM-based U-Net architectures for 3D medical image segmentation. Convolutional U-Nets afford O(n) local mixing per layer but lack explicit global context; transformers provide global reasoning at O(n^2) cost in sequence length $n$. State-space models (SSMs), such as Mamba, offer $O(n)$ global propagation per block. Yet, existing medical SSM segmenters rely on fixed scan patterns and large parameter budgets. Dynamic Adaptive Scan (DAS), which learns data-dependent reordering before selective scan, has not been applied to medical imaging or extended to 3D volumes. We propose DAMamba-UNet3D, a hybrid encoder-decoder that integrates tri-plane 3D-DAS blocks at encoder stages E2-E4 while retaining convolutions elsewhere (~5.3M parameters). On BraTS 2020 five-fold cross-validation, DAMamba-UNet3D achieves mean Dice 0.815+/-0.013 (full-volume per-case evaluation) at ~13x lower parameter cost than SegMamba (0.824+\-0.014, ~70M). At comparable scale, DAMamba-L (~70M), a wide DAS-native variant with encoder-only DAMamba and a convolutional bottleneck, reaches 0.829+\-0.012, surpassing retrained SegMamba by 0.5pt. Component ablations show that encoder-only DAS placement is critical as bottleneck and decoder SSM blocks lower Dice. Together, the results suggest that learned tri-plane DAS in a hybrid U-Net is competitive with, and under our large-scale design may improve upon, SegMamba's fixed Tri-orientated Mamba (ToM) scanning on BraTS 2020. Code: https://github.com/marafathussain/DAMamba-UNet3D.

Related papers

• BiSegMamba: Efficient Bidirectional Tri-Oriented Mamba for 3D Medical Image Segmentation [6.344197178127241]
  We propose BiSegMamba, an efficient tri-oriented Mamba network for 3D medical image segmentation.<n>BiSegMamba generalizes well across vascular, cardiac, brain tumor, and abdominal multi-organ segmentation tasks.<n>Compared with SegMamba-V2, BiSegMamba achieves slightly better performance on BraTS2023 and clear improvements on ACDC and the carotid dataset.
  arXiv  Detail & Related papers  (2026-05-29T08:13:22Z)
• DALight-3D: A Lightweight 3D U-Net for Brain Tumor Segmentation from Multi-Modal MRI [0.0]
  DALight-3D is a compact 3D U-Net variant that combines depthwise separable 3D convolutions, identifier-conditioned normalization, cross-slice attention, and adaptive skip fusion.<n>Results show DALight-3D offers a favorable accuracy-efficiency trade-off within the present benchmark setting.
  arXiv  Detail & Related papers  (2026-05-06T05:54:44Z)
• A Hybrid Mamba-SAM Architecture for Efficient 3D Medical Image Segmentation [0.4358626952482685]
  Mamba-SAM is a novel and efficient hybrid architecture that combines a frozen SAM encoder with the linear-time efficiency and long-range modeling capabilities of Mamba-based State Space Models (SSMs)<n>We introduce Multi-Frequency Gated Convolution (MFGC), which enhances feature representation by jointly analyzing spatial and frequency-domain information via 3D discrete cosine transforms and adaptive gating.<n>The dual-branch Mamba-SAM-Base model achieves a mean Dice score of 0.906, comparable to UNet++ (0.907), while outperforming all baselines on Myocardium (0.910) and Left Ventric
  arXiv  Detail & Related papers  (2026-01-31T10:51:17Z)
• Multimodal Visual Surrogate Compression for Alzheimer's Disease Classification [69.87877580725768]
  Multimodal Visual Surrogate Compression (MVSC) learns to compress and adapt large 3D sMRI volumes into compact 2D features.<n>MVSC has two key components: a Volume Context that captures global cross-slice context under textual guidance, and an Adaptive Slice Fusion module that aggregates slice-level information in a text-enhanced, patch-wise manner.
  arXiv  Detail & Related papers  (2026-01-29T13:05:46Z)
• D3D-VLP: Dynamic 3D Vision-Language-Planning Model for Embodied Grounding and Navigation [66.7166217399105]
  Embodied agents face a critical dilemma that end-to-end models lack interpretability and explicit 3D reasoning.<n>Our model introduces two key innovations: 1) A Dynamic 3D Chain-of-Thought (3D CoT) that unifies planning, grounding, navigation, and question answering within a single 3D-VLM and CoT pipeline; 2) A Synergistic Learning from Fragmented Supervision (SLFS) strategy, which uses a masked autoregressive loss to learn from massive and partially-annotated hybrid data.
  arXiv  Detail & Related papers  (2025-12-14T09:53:15Z)
• Flatten Wisely: How Patch Order Shapes Mamba-Powered Vision for MRI Segmentation [4.9921974350575296]
  Vision Mamba models promise transformer-level performance at linear computational cost.<n>Their reliance on serializing 2D images into 1D sequences introduces a critical, yet overlooked, design choice: the patch scan order.<n>This paper presents the first systematic study of how scan order impacts MRI segmentation.
  arXiv  Detail & Related papers  (2025-07-15T21:37:37Z)
• UniMamba: Unified Spatial-Channel Representation Learning with Group-Efficient Mamba for LiDAR-based 3D Object Detection [53.785766442201094]
  Recent advances in LiDAR 3D detection have demonstrated the effectiveness of Transformer-based frameworks in capturing the global dependencies from point cloud spaces.<n>Due to the considerable number of 3D voxels and quadratic complexity of Transformers, multiple sequences are grouped before feeding to Transformers, leading to a limited receptive field.<n>Inspired by the impressive performance of State Space Models (SSM) achieved in the field of 2D vision tasks, we propose a novel Unified Mamba (UniMamba)<n>Specifically, a UniMamba block is designed which mainly consists of locality modeling, Z-order serialization and local-global sequential aggregator.
  arXiv  Detail & Related papers  (2025-03-15T06:22:31Z)
• MedSegMamba: 3D CNN-Mamba Hybrid Architecture for Brain Segmentation [15.514511820130474]
  We develop a 3D patch-based hybrid CNN-Mamba model for subcortical brain segmentation. Our model's performance was validated against several benchmarks.
  arXiv  Detail & Related papers  (2024-09-12T02:19:19Z)
• Gamba: Marry Gaussian Splatting with Mamba for single view 3D reconstruction [153.52406455209538]
  Gamba is an end-to-end 3D reconstruction model from a single-view image. It completes reconstruction within 0.05 seconds on a single NVIDIA A100 GPU.
  arXiv  Detail & Related papers  (2024-03-27T17:40:14Z)
• Asymmetric 3D Context Fusion for Universal Lesion Detection [55.61873234187917]
  3D networks are strong in 3D context yet lack supervised pretraining. Existing 3D context fusion operators are designed to be spatially symmetric, performing identical operations on each 2D slice like convolutions. We propose a novel asymmetric 3D context fusion operator (A3D), which uses different weights to fuse 3D context from different 2D slices.
  arXiv  Detail & Related papers  (2021-09-17T16:25:10Z)
• 3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI [55.97060983868787]
  We investigate whether using increased spatial context by using MRI volumes combined with spatial erasing leads to improved unsupervised anomaly segmentation performance. We compare 2D variational autoencoder (VAE) to their 3D counterpart, propose 3D input erasing, and systemically study the impact of the data set size on the performance. Our best performing 3D VAE with input erasing leads to an average DICE score of 31.40% compared to 25.76% for the 2D VAE.
  arXiv  Detail & Related papers  (2021-09-14T09:17:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.