{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:41:58Z","timestamp":1760060518302,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T00:00:00Z","timestamp":1757203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Soybean rust, caused by the fungus Phakopsora pachyrhizi, is recognized as the most devastating disease affecting soybean crops worldwide. In practical applications, performing accurate Phakopsora pachyrhizi segmentation (PPS) is essential for elucidating the morphodynamics of soybean rust, thereby facilitating effective prevention strategies and advancing research on related soybean diseases. Despite its importance, studies focusing on PPS-related datasets and the automatic segmentation of Phakopsora pachyrhizi remain limited. To address this gap, we propose an efficient semantic segmentation model named MS-UNet (Multi-Scale Confusion UNet Network). In the hierarchical Vision Transformer (ViT) module, the feature maps are down-sampled to reduce the lengths of the keys (K) and values (V), thereby minimizing the computational complexity. This design not only lowers the resource demands of the transformer but also enables the network to effectively capture multi-scale and high-resolution features. Additionally, depthwise separable convolutions are employed to compensate for positional information, which alleviates the difficulty the ViT faces in learning robust positional encodings, especially for small datasets. Furthermore, MS-UNet dynamically generates labels for both hard-to-segment and easy-to-segment regions, compelling the network to concentrate on more challenging locations and improving its overall segmentation capability. Compared to the existing state-of-the-art methods, our approach achieves a superior performance in PPS tasks.<\/jats:p>","DOI":"10.3390\/s25175582","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T11:51:12Z","timestamp":1757332272000},"page":"5582","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MS-UNet: A Hybrid Network with a Multi-Scale Vision Transformer and Attention Learning Confusion Regions for Soybean Rust Fungus"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7171-5239","authenticated-orcid":false,"given":"Tian","family":"Liu","sequence":"first","affiliation":[{"name":"School of Electromechanical Engineering, Beijing Information Science and Technology University, Beijing 100192, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangzheng","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Optoelectronics, Beijing Information Science and Technology University, Beijing 100192, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiulong","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electromechanical Engineering, Beijing Information Science and Technology University, Beijing 100192, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingquan","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Electromechanical Engineering, Beijing Information Science and Technology University, Beijing 100192, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0005-5445","authenticated-orcid":false,"given":"Peng","family":"Su","sequence":"additional","affiliation":[{"name":"School of Electromechanical Engineering, Beijing Information Science and Technology University, Beijing 100192, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengwei","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.biosystemseng.2010.06.004","article-title":"Image processing methods for quantitatively detecting soybean rust from multispectral images","volume":"107","author":"Cui","year":"2010","journal-title":"Biosyst. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1111\/tpj.14426","article-title":"The Arabidopsis non-host defence-associated coumarin scopoletin protects soybean from Asian soybean rust","volume":"99","author":"Beyer","year":"2019","journal-title":"Plant J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6424","DOI":"10.1038\/s41467-021-26577-1","article-title":"Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease","volume":"12","author":"Cavanagh","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_5","first-page":"7281","article-title":"Hrformer: High-resolution vision transformer for dense predict","volume":"34","author":"Yuan","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s13735-017-0141-z","article-title":"A review of semantic segmentation using deep neural networks","volume":"7","author":"Guo","year":"2018","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.knosys.2024.112854","article-title":"Multi-UNet: An effective Multi-U convolutional networks for semantic segmentation","volume":"309","author":"Zhao","year":"2025","journal-title":"Knowl. Based Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tang, M., He, Y., Aslam, M., Akpokodje, E., and Jilani, S.F. (2025). Enhanced U-Net for Improved Semantic Segmentation in Landslide Detection. Sensors, 25.","DOI":"10.3390\/s25092670"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.neucom.2018.03.037","article-title":"Methods and datasets on semantic segmentation: A review","volume":"304","author":"Yu","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","article-title":"Learning hierarchical features for scene labeling","volume":"35","author":"Farabet","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1109\/TIP.2005.852470","article-title":"Toward automatic phenotyping of developing embryos from videos","volume":"14","author":"Ning","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Chen, X., and Wang, J. (2020). Object-contextual representations for semantic segmentation. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-58539-6_11"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Siam, M., Elkerdawy, S., Jagersand, M., and Yogamani, S. (2017, January 16\u201319). Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317714"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 15\u201320). Deep high-resolution representation learning for human pose estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_17","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yurtkulu, S.C., \u015eahin, Y.H., and Unal, G. (2019, January 24\u201326). Semantic segmentation with extended DeepLabv3 architecture. Proceedings of the 2019 27th Signal Processing and Communications Applications Conference (SIU), Sivas, Turkey.","DOI":"10.1109\/SIU.2019.8806244"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Vaswani, A., Ramachandran, P., Srinivas, A., Parmar, N., Hechtman, B., and Shlens, J. (2021, January 20\u201325). Scaling local self-attention for parameter efficient visual backbones. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01270"},{"key":"ref_21","unstructured":"Ramachandran, P., Parmar, N., Vaswani, A., Bello, I., Levskaya, A., and Shlens, J. (2019, January 8\u201314). Stand-alone self-attention in vision models. Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_22","unstructured":"Chen, Y., Kalantidis, Y., Li, J., Yan, S., and Feng, J. (2018, January 3\u20138). A2-nets: Double attention networks. Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montr\u00e9al, DC, Canada."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-end object detection with transformers. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107609","DOI":"10.1016\/j.compag.2022.107609","article-title":"FBoT-Net: Focal bottleneck transformer network for small green apple detection","volume":"205","author":"Sun","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107651","DOI":"10.1016\/j.compag.2023.107651","article-title":"Combining convolutional and vision transformer structures for sheep face recognition","volume":"205","author":"Li","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111505","DOI":"10.1016\/j.patcog.2025.111505","article-title":"CGViT: Cross-image GroupViT for zero-shot semantic segmentation","volume":"164","author":"Jiang","year":"2025","journal-title":"Pattern Recognit."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Touazi, F., Gaceb, D., Boudissa, N., and Assas, S. (2024). Enhancing Breast Mass Cancer Detection Through Hybrid ViT-Based Image Segmentation Model. International Conference on Computing Systems and Applications, Springer.","DOI":"10.1007\/978-3-031-71848-9_12"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","article-title":"H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes","volume":"37","author":"Li","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bosma, M., Dushatskiy, A., Grewal, M., Alderliesten, T., and Bosman, P. (2022). Mixed-Block Neural Architecture Search for Medical Image Segmentation. arXiv.","DOI":"10.1117\/12.2611428"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, C., MacGillivray, T., Macnaught, G., Yang, G., and Newby, D. (2018). A two-stage 3D Unet framework for multi-class segmentation on full resolution image. arXiv.","DOI":"10.1007\/978-3-030-12029-0_21"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/JBHI.2019.2912935","article-title":"Fully dense UNet for 2-D sparse photoacoustic tomography artifact removal","volume":"24","author":"Guan","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xiao, X., Lian, S., Luo, Z., and Li, S. (2018, January 19\u201321). Weighted res-unet for high-quality retina vessel segmentation. Proceedings of the 2018 9th International Conference on Information Technology in Medicine and Education (ITME), Hangzhou, China.","DOI":"10.1109\/ITME.2018.00080"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"194551","DOI":"10.1109\/ACCESS.2020.3033273","article-title":"Retinal vessel segmentation combined with generative adversarial networks and dense U-Net","volume":"8","author":"Guo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"106818","DOI":"10.1016\/j.compag.2022.106818","article-title":"Automatic segmentation of cattle rib-eye area in ultrasound images using the UNet++ deep neural network","volume":"195","author":"Gomes","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"107754","DOI":"10.1016\/j.compag.2023.107754","article-title":"Weighted skip-connection feature fusion: A method for augmenting UAV oriented rice panicle image segmentation","volume":"207","author":"Xiao","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1007\/s13198-025-02762-z","article-title":"Enhanced BT segmentation with modified U-Net architecture: A hybrid optimization approach using CFO-SFO algorithm","volume":"16","author":"Yogalakshmi","year":"2025","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2947","DOI":"10.3934\/era.2025129","article-title":"DPUSegDiff: A Dual-Path U-Net Segmentation Diffusion model for medical image segmentation","volume":"33","author":"Fan","year":"2025","journal-title":"Electron. Res. Arch."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"128534","DOI":"10.1016\/j.neucom.2024.128534","article-title":"Shape-intensity-guided U-net for medical image segmentation","volume":"610","author":"Fan","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.P., and Shao, L. (2021, January 10\u201317). Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"ref_40","unstructured":"Chu, X., Tian, Z., Zhang, B., Wang, X., Wei, X., Xia, H., and Shen, C. (2021). Conditional Positional Encodings for Vision Transformers. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.neucom.2021.03.085","article-title":"A Hybrid Deep Segmentation Network for Fundus Vessels via Deep-Learning Framework","volume":"448","author":"Yang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_42","unstructured":"Gao, Y., Zhou, M., and Metaxas, D.N. (October, January 27). UTNet: A hybrid transformer architecture for medical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, France."},{"key":"ref_43","unstructured":"Xie, E., Wang, W., Yu, Z., An kumar, A., \u00c1lvarez, J.M., and Luo, P. (2021, January 6\u201314). SegFormer: Simple and efficient design for semantic segmentation with transformers. Proceedings of the Neural Information Processing Systems, Online."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., and Wang, M. (2022, January 23). Swin-Unet: Unet-like pure transformer for medical image segmentation. Proceedings of the ECCV Workshops, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"ref_45","first-page":"9210","article-title":"MobileViT: Light-weight, general-purpose, and mobile-friendly vision transformer","volume":"21","author":"Mehta","year":"2021","journal-title":"Sensors"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/17\/5582\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:41:28Z","timestamp":1760035288000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/17\/5582"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,7]]},"references-count":46,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["s25175582"],"URL":"https:\/\/doi.org\/10.3390\/s25175582","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2025,9,7]]}}}