journal article·2025-07

DGFE-Mamba: Mamba-Based 2D Image Segmentation Network

Junding Sun, Kaixin Chen, Shuihua Wang, Yudong Zhang, Zhaozhao Xu, Xiaosheng Wu, Chaosheng Tang

Journal of Bionic Engineering· Volume 22· Issue 4
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Abstract

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Research context

OpenAlex topics: Advanced Neural Network Applications · Medical Image Segmentation Techniques · Digital Imaging for Blood Diseases

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References (44)

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1. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015 (MICCAI), Munich, Germany, 234–241. https://doi.org/10.48550/arXiv.1505.0459710.48550/arXiv.1505.04597 ↗
2. Siddique, N., Paheding, S., Elkin, C. P., & Devabhaktuni, V. (2021). U-Net and its variants for medical image segmentation: A review of theory and applications. Ieee Access: Practical Innovations, Open Solutions, 9, 82031–82057. https://doi.org/10.1109/ACCESS.2021.308602010.1109/ACCESS.2021.3086020 ↗
3. Zhou, Z. W., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. M. (2019). UNet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging, 39(6), 1856–1867. https://doi.org/10.1109/TMI.2019.295960910.1109/TMI.2019.2959609 ↗
4. Huang, H. M., Lin, L. F., Tong, R. F., Hu, H. J., Zhang, Q. W., Iwamoto, Y., Han, X. H., Chen, Y. W., & Wu, J. (2020). UNet 3+: A full-scale connected unet for medical image segmentation. In: 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 1055–1059. https://doi.org/10.1109/ICASSP40776.2020.905340510.1109/ICASSP40776.2020.9053405 ↗
5. Xie, X. W., Zhang, W. D., Wang, H. D., Li, L. Q., Feng, Z. Y., Wang, Z. Z., Wang, Z. M., & Pan, X. P. (2021). Dynamic adaptive residual network for liver CT image segmentation. Computers and Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2021.10702410.1016/j.compeleceng.2021.107024 ↗
6. Rahman, H., Bukht, T. F. N., Imran, A., Tariq, J., Tu, S. S., & Alzahrani, A. (2022). A deep learning approach for liver and tumor segmentation in CT images using resunet. Bioengineering, 9(8), 368. https://doi.org/10.3390/bioengineering908036810.3390/bioengineering9080368 ↗
7. Maji, D., Sigedar, P., & Singh, M. (2022). Attention Res-UNet with guided decoder for semantic segmentation of brain tumors. Biomedical Signal Processing and Control, 71, 103077. https://doi.org/10.1016/j.bspc.2021.10307710.1016/j.bspc.2021.103077 ↗
8. Sabir, M. W., Khan, Z., Saad, N. M., Khan, D. M., Al-Khasawneh, M. A., Perveen, K., Qayyum, A., & Ali, A., S. S (2022). Segmentation of liver tumor in CT scan using ResU-Net. Applied Sciences, 12(17), 8650. https://doi.org/10.3390/app1217865010.3390/app12178650 ↗
9. Li, X. M., Chen, H., Qi, X. J., Dou, Q., Fu, C. W., & Heng, P. A. (2018). H-DenseUNet: Hybrid densely connected Unet for liver and tumor segmentation from CT volumes. IEEE Transactions on Medical Imaging, 37(12), 2663–2674. https://doi.org/10.1109/TMI.2018.284591810.1109/TMI.2018.2845918 ↗
10. Safarov, S., & Whangbo, T. K. (2021). A-DenseUNet: Adaptive densely connected unet for polyp segmentation in colonoscopy images with atrous convolution. Sensors,21(4), 1441. https://doi.org/10.3390/s2104144110.3390/s21041441 ↗
11. Cai, S. J., Tian, Y. X., Lui, H., Zeng, H. S., Wu, Y., & Chen, G. N. (2020). Dense-UNet: A novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network. Quantitative Imaging in Medicine and Surgery, 10(6), 1275. https://doi.org/10.21037/qims-19-109010.21037/qims-19-1090 ↗
12. Cao, Y., Liu, S. G., Peng, Y., & Li, J. (2020). DenseUNet: Densely connected Unet for electron microscopy image segmentation. IET Image Processing, 14(12), 2682–2689. https://doi.org/10.1049/iet-ipr.2019.152710.1049/iet-ipr.2019.1527 ↗
13. Liu, S., Luo, Z., & Fu, W. N. (2024). FCDNet: Fuzzy cognition-based dynamic fusion network for multimodal sentiment analysis. IEEE Transactions on Fuzzy Systems, 1–12. https://doi.org/10.1109/TFUZZ.2024.340773910.1109/TFUZZ.2024.3407739 ↗
14. Luo, Z., Fu, W. N., Liu, S., Anwar, S., Saqib, M., Bakshi, S., & Muhammad, K. (2024). CEFDET: Cognitive effectiveness network based on fuzzy inference for action detection. In: Proceedings of the 32nd ACM International Conference on Multimedia (ACM MM). Melbourne, Australia, https://doi.org/10.1145/3664647.368122610.1145/3664647.3681226 ↗
15. Zeng, Z. T., Xie, W. H., Zhang, Y. Z., & Lu, Y. (2019). RIC-UNet: An improved neural network based on Unet for nuclei segmentation in histology images. Ieee Access: Practical Innovations, Open Solutions, 7, 21420–21428. https://doi.org/10.1109/ACCESS.2019.289692010.1109/ACCESS.2019.2896920 ↗
16. Song, H. J., Wang, Y. F., Zeng, S. J., Guo, X. Y., & Li, Z. H. (2023). OAU-Net: Outlined attention u-net for biomedical image segmentation. Biomedical Signal Processing and Control, 79, 104038. https://doi.org/10.1016/j.bspc.2022.10403810.1016/j.bspc.2022.104038 ↗
17. Gu, Z. W., Cheng, J., Fu, H. Z., Zhou, K., Hao, H. Y., Zhao, Y. T., Zhang, T. Y., Gao, S. H., & Liu, J. (2019). CE-Net: Context encoder network for 2D medical image segmentation. IEEE Transactions on Medical Imaging, 38(10), 2281–2292. https://doi.org/10.1109/TMI.2019.290356210.1109/TMI.2019.2903562 ↗
18. Ni, J. J., Wu, J. H., Tong, J., Chen, Z. M., & Zhao, J. P. (2020). GC-Net: Global context network for medical image segmentation. Computer Methods and Programs in Biomedicine, 190, 105121. https://doi.org/10.1016/j.cmpb.2019.10512110.1016/j.cmpb.2019.105121 ↗
19. Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, 7132–7141. https://doi.org/10.1109/CVPR.2018.0074510.1109/CVPR.2018.00745 ↗
20. Woo, S., Park, J., Lee, J. Y., & Kweon, I. S. (2018). CBAM: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 3–19. https://doi.org/10.1007/978-3-030-01234-2_110.1007/978-3-030-01234-2_1 ↗
21. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X. H., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (ICLR), Virtual Event, Austria. https://doi.org/10.48550/arXiv.2010.1192910.48550/arXiv.2010.11929 ↗
22. Liu, Z., Lin, Y. T., Cao, Y., Hu, H., Wei, Y. X., Zhang, Z., Lin, S., & Guo, B. N. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, Canada, 10012–10022. https://doi.org/10.1109/ICCV48922.2021.0098710.1109/ICCV48922.2021.00987 ↗
23. Sun, W. W., Chen, J. G., Yan, L., Lin, J. Z., Pang, Y., & Zhang, G. (2022). COVID-19 CT image segmentation method based on Swin transformer. Frontiers in Physiology, 13, 981463. https://doi.org/10.3389/fphys.2022.98146310.3389/fphys.2022.981463 ↗
24. Jiang, Y., Zhang, Y., Lin, X., Dong, J. K., Cheng, T. T., & Liang, J. (2022). SwinBTS: A method for 3D multimodal brain tumor segmentation using swin transformer. Brain Sciences,12(6), 797. https://doi.org/10.3390/brainsci1206079710.3390/brainsci12060797 ↗
25. Zhang, C., Wan, H. C., Shen, X. Y., & Wu, Z. Z. (2022). PVT: Point-voxel transformer for point cloud learning. International Journal of Intelligent Systems, 37(12), 11985–12008. https://doi.org/10.1002/int.2307310.1002/int.23073 ↗
26. Wang, W. H., Xie, E., Li, X., Fan, D. P., Song, K. T., Liang, D., Lu, T., Luo, P., & Shao, L. (2022). Pvt v2: Improved baselines with pyramid vision transformer. Computational Visual Media, 8(3), 415–424. https://doi.org/10.1007/s41095-022-0274-810.1007/s41095-022-0274-8 ↗
27. Lu, D. N., Xie, Q., Gao, K., Xu, L. L., & Li, J. (2022). 3DCTN: 3D convolution-transformer network for point cloud classification. IEEE Transactions on Intelligent Transportation Systems, 23(12), 24854–24865. https://doi.org/10.1109/TITS.2022.319883610.1109/TITS.2022.3198836 ↗
28. Chen, J. N., Lu, Y. Y., Yu, Q. H., Luo, X. D., Adeli, E., Wang, Y., Lu, L., Yuille, A. L., & Zhou, Y. Y. (2021). TransUNet: Transformers make strong encoders for medical image segmentation. Arxiv Preprint Arxiv: 2102 04306. https://doi.org/10.48550/arXiv.2102.0430610.48550/arXiv.2102.04306 ↗
29. Gao, L., Liu, H., Yang, M. H., Chen, L., Wan, Y. L., Xiao, Z. Q., & Qian, Y. R. (2021). STransFuse: Fusing Swin transformer and convolutional neural network for remote sensing image semantic segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), 14, 10990–11003. https://doi.org/10.1109/JSTARS.2021.311965410.1109/JSTARS.2021.3119654 ↗
30. Pham, T. H., Li, X. Q., & Nguyen, K. D. (2024). Seunet-Trans: A simple yet effective unet-transformer model for medical image segmentation. Ieee Access: Practical Innovations, Open Solutions, 12, 122139–122154. https://doi.org/10.1109/ACCESS.2024.345130410.1109/ACCESS.2024.3451304 ↗
31. Sun, G. Q., Pan, Y. Z., Kong, W. K., Xu, Z. C., Ma, J. H., Racharak, T., Nguyen, L. M., & Xin, J. Y. (2024). DA-TransUNet: Integrating Spatial and channel dual attention with transformer u-net for medical image segmentation. Frontiers in Bioengineering and Biotechnology, 12, 1398237. https://doi.org/10.3389/fbioe.2024.139823710.3389/fbioe.2024.1398237 ↗
32. Cao, H., Wang, Y. Y., Chen, J., Jiang, D. S., Zhang, X. P., Tian, Q., & Wang, M. N. (2022). Swin-UNet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision (ECCV), Tel Aviv, Israel, 205–218. https://doi.org/10.1007/978-3-031-25066-8_910.1007/978-3-031-25066-8_9 ↗
33. Pan, S. M., Liu, X., Xie, N. D., & Chong, Y. W. (2023). EG-TransUNet: A transformer-based u-net with enhanced and guided models for biomedical image segmentation. Bmc Bioinformatics, 24(1), 85. https://doi.org/10.1186/s12859-023-05196-110.1186/s12859-023-05196-1 ↗
34. Lin, A. L., Chen, B. Z., Xu, J. Y., Zhang, Z., Lu, G. M., & Zhang, D. (2022). DS-TransUNet: Dual Swin transformer u-net for medical image segmentation. IEEE Transactions on Instrumentation and Measurement, 71, 1–15. https://doi.org/10.1109/TIM.2022.317899110.1109/TIM.2022.3178991 ↗
35. Rahman, M. M., & Marculescu, R. (2023). Medical image segmentation via cascaded attention decoding. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 6222–6231. https://doi.org/10.1109/WACV56688.2023.0061610.1109/WACV56688.2023.00616 ↗
36. Gu, A., & Dao, T. (2023). Mamba: Linear-time sequence modeling with selective state spaces. Preprint retrieved from. https://doi.org/10.48550/arXiv.2312.0075210.48550/arXiv.2312.00752 ↗
37. Zhu, L. H., Liao, B. C., Zhang, Q., Wang, X. L., Liu, W. Y., & Wang, X. G. (2024). Vision Mamba: Efficient visual representation learning with bidirectional state space model. In: Proceedings of the 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria. Preprint retrieved fom https://doi.org/10.48550/arXiv.2401.0941710.48550/arXiv.2401.09417 ↗
38. Zhang, M. Y., Yu, Y., Jin, S., Gu, L. M., Ling, T. S., & Tao, X. P. (2024). VM-UNET-V2: Rethinking vision mamba unet for medical image segmentation. In: Proceedings of the 20th International Symposium on Bioinformatics Research and Applications (ISBRA 2024), Kunming, China, 335–346. https://doi.org/10.48550/arXiv.2403.0915710.48550/arXiv.2403.09157 ↗
39. Wang, Z. Y., Zheng, J. Q., Zhang, Y. C., Cui, G., & Li, L. (2024). Mamba-UNet: Unet-like pure visual Mamba for medical image segmentation. Arxiv Preprint Arxiv: 2402 05079. https://doi.org/10.48550/arXiv.2402.0507910.48550/arXiv.2402.05079 ↗
40. Zhang, X. X., & Mu, W. S. (2024). Gmamba: State space model with Convolution for grape leaf disease segmentation. Computers and Electronics in Agriculture, 225, 109290. https://doi.org/10.1016/j.compag.2024.10929010.1016/j.compag.2024.109290 ↗
41. Zou, B. F., Huang, X. R., Jiang, Y. T., Jin, K., & Sun, Y. Q. (2024). Demambanet: Deformable Convolution and Mamba integration network for high-precision segmentation of ambiguously defined dental radicular boundaries. Sensors (Basel, Switzerland), 24(14), 4748. https://doi.org/10.3390/s2414474810.3390/s24144748 ↗
42. Hu, M., Zhang, Y. R., Xue, H. J., Lv, H., & Han, S. P. (2024). Mamba-and ResNet-based dual-branch network for ultrasound thyroid nodule segmentation. Bioengineering, 11(10), 1047. https://doi.org/10.3390/bioengineering1110104710.3390/bioengineering11101047 ↗
43. Liu, J. R., Yang, H., Zhou, H. Y., Yu, L. Q., Liang, Y., Yu, Y. Z., Zhang, S. T., Zhang, H. R., & Wang, S. S. (2024). Swin-UMamba†: Adapting mamba-based vision foundation models for medical image segmentation. IEEE Transactions on Medical Imaging, https://doi.org/10.1109/TMI.2024.350869810.1109/TMI.2024.3508698 ↗
44. Li, G. J., Huang, Q. H., Wang, W., & Liu, L. Z. (2025). Selective and multi-scale fusion Mamba for medical image segmentation. Expert Systems with Applications, 261, 125518. https://doi.org/10.1016/j.eswa.2024.12551810.1016/j.eswa.2024.125518 ↗