Abstract
Medical image segmentation is critical for computer-aided medical diagnosis, both convolutional neural networks and the Transformer model have received widespread attention. However, convolutional Neural Networks and Transformers are each limited by global information and computational complexity. Recent studies have shown that the state space model (SSM) represented by Vision Mamba is able to maintain linear computational complexity and to simulate long-range interactions. Inspired by this, we propose the High-order Morphological Method Vision Mamba (HMM-VMamba) in this paper to further enhance the focus on lesion regions. Among its components, the proposed High-order Morphological Method Selective-Scan-2D (HMM-SS2D) exploits high-order information channel interactions to explore morphological knowledge. Moreover, the combination of Local Selective-Scan-2D, External Multi-scale Attention Mechanism, and Convolutional Neural Network improves the capability of local boundary feature learning. We evaluated comparison and ablation experiments on three open-source medical image datasets, and the results demonstrate that HMM-VMamba exhibits strong learning capabilities in medical image segmentation. Github
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References
Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., Vilariño, F.: Wm-dova maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Computerized Med. Imaging Graph. 43, 99–111 (2015)
Cheng, J.Z., et al.: Computer-aided diagnosis with deep learning architecture: applications to breast lesions in us images and pulmonary nodules in ct scans. Sci. Rep. 6(1), 24454 (2016)
Codella, N.C., et al.: Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 168–172. IEEE (2018)
Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023)
Hu, S., Liao, Z., Xia, Y.: Devil is in channels: Contrastive single domain generalization for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 14–23. Springer (2023)
Liu, Y., et al.: Vmamba: visual state space model. arXiv preprint arXiv:2401.10166 (2024)
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
Nekoozadeh, A., Ahmadzadeh, M.R., Mardani, Z.: Multiscale attention via wavelet neural operators for vision transformers. arXiv preprint arXiv:2303.12398 (2023)
Oktay, O., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. pp. 234–241. Springer (2015)
Ruan, J., Xiang, S.: Vm-unet: Vision mamba unet for medical image segmentation. arXiv preprint arXiv:2402.02491 (2024)
Ruan, J., Xiang, S., Xie, M., Liu, T., Fu, Y.: Malunet: a multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1150–1156. IEEE (2022)
Sirinukunwattana, K., Pluim, J.P., Chen, H., Qi, X., Heng, P.A., Guo, Y.B., Wang, L.Y., Matuszewski, B.J., Bruni, E., Sanchez, U., et al.: Gland segmentation in colon histology images: the glas challenge contest. Med. Image Anal. 35, 489–502 (2017)
Wu, R., Liang, P., Huang, X., Shi, L., Gu, Y., Zhu, H., Chang, Q.: Mhorunet: High-order spatial interaction unet for skin lesion segmentation. Biomed. Signal Process. Control 88, 105517 (2024)
Wu, R., Liu, Y., Liang, P., Chang, Q.: H-vmunet: igh-order vision mamba unet for medical image segmentation. arXiv preprint arXiv:2403.13642 (2024)
Wu, R., Liu, Y., Liang, P., Chang, Q.: Ultralight vm-unet: parallel vision mamba significantly reduces parameters for skin lesion segmentation. arXiv preprint arXiv:2403.20035 (2024)
Yuan, H., Chen, L., He, X.: Mmunet: morphological feature enhancement network for colon cancer segmentation in pathological images. Biomed. Signal Process. Control 91, 105927 (2024)
Zhang, M., Yu, Y., Gu, L., Lin, T., Tao, X.: Vm-unet-v2 rethinking vision mamba unet for medical image segmentation. arXiv preprint arXiv:2403.09157 (2024)
Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., Wang, X.: Vision mamba: efficient visual representation learning with bidirectional state space model. arXiv preprint arXiv:2401.09417 (2024)
Acknowledgement
This study was funded by Postgraduate Scientific Research Innovation Project of Hunan Province, China (CX20240836). The Natural Science Foundation of Hunan province, China (2024JJ7429 and 2022JJ3031), Province Undergraduate Education Teaching Reform Research Project, China (202401000802), and Guangxi youth science and technology innovation talents training project, china (2021AC19421).
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Yao, Y. et al. (2025). HMM-VMamba: High-Order Morphological Method Vision Mamba for Medical Image Segmentation. In: Hadfi, R., Anthony, P., Sharma, A., Ito, T., Bai, Q. (eds) PRICAI 2024: Trends in Artificial Intelligence. PRICAI 2024. Lecture Notes in Computer Science(), vol 15283. Springer, Singapore. https://doi.org/10.1007/978-981-96-0122-6_33
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DOI: https://doi.org/10.1007/978-981-96-0122-6_33
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