ArticleScientific reports2024
A mixed Mamba U-net for prostate segmentation in MR images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Learning CT-to-electron-density mapping for radiotherapy treatment planning using neural networks.Radiation oncology (London, England) · 2026Article
- Study on mmage defect recognition and classification of power transmission equipment based on lightweight model residual Mamba.Scientific reports · 2026Article
- Enhancing vision Mamba with two-dimensional position embedding and multiscale fusion for medical image segmentation.Quantitative imaging in medicine and surgery · 2026Article
- Automated segmentation of lymph node metastases in colorectal cancer based on convolutional KAN.Open medicine (Warsaw, Poland) · 2026Article
- An investigation of race bias in deep learning-based segmentation of prostate MRI images.Scientific reports · 2025Article
- Comparative analysis of sandstone microtomographic image segmentation using advanced convolutional neural networks with pixelwise and physical accuracy evaluation.Scientific reports · 2025Article
- Boundary-enhanced local-global collaborative network for medical image segmentation.Scientific reports · 2025Article
- RaNet: a residual attention network for accurate prostate segmentation in T2-weighted MRI.Frontiers in medicine · 2025Article
- Mamba- and ResNet-Based Dual-Branch Network for Ultrasound Thyroid Nodule Segmentation.Bioengineering (Basel, Switzerland) · 2024Article
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Authors and funding
3 authors.
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Abstract
The diagnosis of early prostate cancer depends on the accurate segmentation of prostate regions in magnetic resonance imaging (MRI). However, this segmentation task is challenging due to the particularities of prostate MR images themselves and the limitations of existing methods. To address these issues, we propose a U-shaped encoder-decoder network MM-UNet based on Mamba and CNN for prostate segmentation in MR images. Specifically, we first proposed an adaptive feature fusion module based on channel attention guidance to achieve effective fusion between adjacent hierarchical features and suppress the interference of background noise. Secondly, we propose a global context-aware module based on Mamba, which has strong long-range modeling capabilities and linear complexity, to capture global context information in images. Finally, we propose a multi-scale anisotropic convolution module based on the principle of parallel multi-scale anisotropic convolution blocks and 3D convolution decomposition. Experimental results on two public prostate MR image segmentation datasets demonstrate that the proposed method outperforms competing models in terms of prostate segmentation performance and achieves state-of-the-art performance. In future work, we intend to enhance the model's robustness and extend its applicability to additional medical image segmentation tasks.
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