TvaraNet is pro-posed, an extremely lightweight segmentation network designed to preserve boundary fidelity under strict efficiency constraints and achieves competitive or superior boundary-aware performance compared to heavier architectures.
Experimental results demonstrate that LightVM-SparseUNet achieves segmentation competitive with state-of-the-art large-scale models across two authoritative public datasets.
Haojie Fan, Kang Xu, Xiaoyu Hou et al.· Biomedical engineering and p...· 0 citations
Reliable medical image segmentation remains challenging because models must preserve fine boundary details while maintaining global semantic consistency. CNNs capture local structures effectively but have limited long-range modeling ability, whereas Transformer-based methods improve global context at high computational cost. Mamba-based state space models offer efficient long-range modeling, but may weaken high-frequency textures and boundary cues. To address these limitations, we propose DIG-MambaNet, a Dual-path Interactive Guided Mamba Network for medical image segmentation. The network introduces a dual-path complementary modeling block (DCM Block), where a cross-feature spatial interaction module (CSIM) adaptively integrates CNN-based local features and Mamba-based global features. A source image-guided module (SIGM) injects high-frequency information from the original image to compensate for downsampling-induced detail loss, while an inter-layer detail refinement fusion module (IDRFM) improves encoder–decoder feature alignment during reconstruction. Experiments on 2018DSB, ISIC2018, JSUAH-Cerebellum, and CVC-ClinicDB, covering nuclei segmentation in microscopy images, skin lesion segmentation in dermoscopic images, fetal cerebellum segmentation in ultrasound images, and polyp segmentation in colonoscopy images, demonstrate that DIG-MambaNet achieves consistent and competitive performance across diverse target structures and imaging conditions, with improved boundary delineation and favorable overlap-based accuracy compared with representative CNN-, Transformer-, and Mamba-based methods.
Yongkang Zhu, Tianyu Yu, Hongmei Li et al.· Journal of Imaging· 0 citations
This work proposes an uncertainty-aware efficient segmentation framework synergizing Mamba state-space models with evidential deep learning, employing a 2D-adapted selective state-space mechanism to capture long-range dependencies with linear complexity O(L), overcoming transformers' quadratic scaling.
SegRWKV, a hybrid architecture that integrates pretrained Vision-RWKV modules in the encoder for efficient global dependency modeling, and a PixelRefinement module in the decoder to improve feature reconstruction and multi-scale alignment, is proposed, highlighting SegRWKV’s superior ability to balance accuracy, efficiency, and scalability.
Tianyu Cao, Mingyao Ma, Haoxiang Zhang et al.· Journal of Computational Des...· 0 citations
Medical image segmentation is a critical task in computer-assisted diagnosis, but accurate delineation remains challenging in sensor-captured dermoscopic and endoscopic images because of low contrast, blurred boundaries, acquisition artifacts, and large appearance variations. Although CNN-based methods are effective in capturing local texture and boundary cues, they often struggle to explicitly model long-range dependencies and global structural relationships. Transformer-based architectures can capture global context, but their self-attention mechanism may become computationally costly when processing high-resolution feature maps. To address these challenges, we propose MGA-UNet, a frequency-aware multi-scale encoder–decoder segmentation framework that integrates wavelet-based frequency decomposition with Mamba-based long-range dependency modelling. Specifically, the Wavelet-Mamba feature extraction backbone (WMB) decomposes features into low- and high-frequency components to enhance boundary-aware representation, the Gated Multi-scale Aggregation Module (GMAM) aggregates parallel multi-scale encoder features and applies a content-dependent gate to the fused response, and the Adaptive Sparse Attention Module (ASAM) refines bottleneck representations with sparse attention for global semantic modelling. Across three independent runs with random seeds 42, 123, and 2026, MGA-UNet achieves mean Dice Similarity Coefficients of 88.92±0.04%, 88.01±0.07%, and 85.91±0.04% on ISIC2018, ISIC2017, and Kvasir-SEG, respectively. These results demonstrate competitive segmentation performance among the compared representative CNN-based, Transformer-based, and Mamba-based methods, including the recent H-VMUNet baseline. These results indicate that frequency-domain decomposition and state-space modelling can complement each other for accurate medical image segmentation, particularly in images with ambiguous boundaries and complex background interference.
Shuai-Kang Qiu, Xuan Wang, Kaile Su et al.· Italian National Conference...· 0 citations
Results indicate the WVM-UNet architecture effectively captures discriminative features for precise medical image segmentation, and demonstrates the competitive performance of the method on multiple public datasets.
Yulong Yang, Wenchao Gao, Zheng-Guo Wu et al.· Journal of Imaging· 0 citations
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