{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T02:52:54Z","timestamp":1790218374952,"version":"4.1.0"},"reference-count":39,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T00:00:00Z","timestamp":1777334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Fund of Hubei Province, China","award":["2026AFB713"],"award-info":[{"award-number":["2026AFB713"]}]},{"name":"Natural Science Fund of Hubei Province, China","award":["2026AFB686"],"award-info":[{"award-number":["2026AFB686"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Early detection and accurate segmentation of colorectal polyps during colonoscopy are crucial for the prevention of colorectal cancer. However, automated polyp segmentation remains challenging because of high inter-class variance, complex intestinal backgrounds, and blurred boundaries. To address these issues while maintaining computational efficiency, DSF-BRNet was developed for endoscopic polyp segmentation. In this framework, a Dual-Gated Semantic Fusion (DSF) module is introduced to reduce spatial misalignment between cross-level features and to provide a more reliable semantic basis for lesion localization. To further alleviate boundary ambiguity, a High-Frequency Boundary Refinement (HBR) module is used to sharpen segmentation contours under aligned semantic guidance. Together, these components form an Align-then-Refine framework in which semantic localization is strengthened before boundary refinement is performed. Experiments on four public benchmark datasets\u2014Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB\u2014showed competitive performance with favorable computational efficiency. Mean Dice scores of 0.943 on CVC-ClinicDB and 0.818 on ETIS-LaribPolypDB were achieved, with 25.55 M parameters and an inference speed of 80.08 FPS. These results indicate that accurate semantic localization and fine boundary preservation can be achieved simultaneously, suggesting that the method may be promising for real-time computer-aided diagnosis (CAD).<\/jats:p>","DOI":"10.3390\/s26092717","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T11:33:37Z","timestamp":1777376017000},"page":"2717","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DSF-BRNet: Dual-Gated Semantic Fusion and Boundary Refinement for Efficient Endoscopic Polyp Segmentation"],"prefix":"10.3390","volume":"26","author":[{"given":"Botao","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science, Yangtze University, Jingzhou 434023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9412-3820","authenticated-orcid":false,"given":"Changqi","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Computer Science, Yangtze University, Jingzhou 434023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Wuxi University, Wuxi 214105, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,28]]},"reference":[{"key":"ref_1","first-page":"17","article-title":"Cancer statistics, 2023","volume":"73","author":"Siegel","year":"2023","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","first-page":"394","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA Cancer J. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1056\/NEJMoa1100370","article-title":"Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths","volume":"366","author":"Zauber","year":"2012","journal-title":"N. Engl. J. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1056\/NEJM199312303292701","article-title":"Prevention of colorectal cancer by colonoscopic polypectomy","volume":"329","author":"Winawer","year":"1993","journal-title":"N. Engl. J. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"64","DOI":"10.5009\/gnl.2012.6.1.64","article-title":"The miss rate for colorectal adenoma determined by quality-adjusted, back-to-back colonoscopies","volume":"6","author":"Ahn","year":"2012","journal-title":"Gut Liver"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1136\/gutjnl-2013-304880","article-title":"Postcolonoscopy colorectal cancers are preventable: A population-based study","volume":"63","author":"Bouwens","year":"2014","journal-title":"Gut"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Nanni, L., Fantozzi, C., Loreggia, A., and Lumini, A. (2023). Ensembles of convolutional neural networks and transformers for polyp segmentation. Sensors, 23.","DOI":"10.20944\/preprints202303.0221.v1"},{"key":"ref_8","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 Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., and Liang, J. (2018). UNet++: A nested U-Net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Ji, G.P., Zhou, T., Chen, G., Fu, H., Shen, J., and Shao, L. (2020, January 4\u20138). PraNet: Parallel reverse attention network for polyp segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI), Lima, Peru.","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9150015","DOI":"10.26599\/AIR.2023.9150015","article-title":"Polyp-PVT: Polyp segmentation with pyramid vision transformers","volume":"2","author":"Dong","year":"2023","journal-title":"CAAI Artif. Intell. Res."},{"key":"ref_12","unstructured":"Ji, G.P., Chou, Y.C., Fan, D.P., Chen, G., Fu, H., Jha, D., and Shao, L. (October, January 27). Progressively normalized self-attention network for video polyp segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI), Strasbourg, France."},{"key":"ref_13","unstructured":"Zhang, R., Lai, G., Huang, X., and Li, S. (October, January 27). Boundary-guided context-aware network for polyp segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI), Strasbourg, France."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Qing, W., Ouyang, Y., and Yin, P. (2025). Research on polyp segmentation via dynamic multi-scale feature fusion and global\u2013local semantic enhancement. Sensors, 25.","DOI":"10.3390\/s25206495"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yun, H., Du, Q., Han, Z., Li, M., Yang, L., Liu, X., Wang, C., and Ma, W. (2025). SGNet: A structure-guided network with dual-domain boundary enhancement and semantic fusion for skin lesion segmentation. Sensors, 25.","DOI":"10.3390\/s25154652"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_17","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 (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_18","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021, January 3\u20137). An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. Proceedings of the International Conference on Learning Representations (ICLR), Vienna, Austria."},{"key":"ref_19","unstructured":"Wei, J., Hu, Y., Zhang, R., Li, Z., Zhou, S.K., and Cui, S. (October, January 27). Shallow attention network for polyp segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI), Strasbourg, France."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Qiu, W., Yang, X., Liu, Z., and Qiu, C. (2025). Advancing real-time polyp detection in colonoscopy imaging: An anchor-free deep learning framework with adaptive multi-scale perception. Sensors, 25.","DOI":"10.3390\/s25247524"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107404","DOI":"10.1016\/j.patcog.2020.107404","article-title":"U-2-Net: Going deeper with nested U-structure for salient object detection","volume":"106","author":"Qin","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Cheng, Y., Pan, B., Jin, G., Yu, D., Ye, M., and Zhang, Q. (2025). A Multi-Modal Attention Fusion Framework for Road Connectivity Enhancement in Remote Sensing Imagery. Mathematics, 13.","DOI":"10.3390\/math13203266"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s41095-022-0274-8","article-title":"PVTv2: Improved baselines with pyramid vision transformer","volume":"8","author":"Wang","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, X., Huo, H., and Zhang, C. (2026). DCANet: Disentanglement and category-aware aggregation for medical image segmentation. Sensors, 26.","DOI":"10.3390\/s26041300"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-Net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Margolin, R., Zelnik-Manor, L., and Tal, A. (2014, January 23\u201328). How to evaluate foreground maps?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.39"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Cheng, M.M., Liu, Y., Li, T., and Borji, A. (2017, January 22\u201329). Structure-measure: A new way to evaluate foreground maps. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.487"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Ji, G.P., Cheng, M.M., and Shao, L. (2018, January 13\u201319). Enhanced-alignment measure for binary foreground map evaluation. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/97"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., de Lange, T., Johansen, D., and Johansen, H.D. (2020, January 5\u20137). Kvasir-SEG: A segmented polyp dataset. Proceedings of the International Conference on Multimedia Modeling (MMM), Daejeon, Republic of Korea.","DOI":"10.1007\/978-3-030-37734-2_37"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","article-title":"WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians","volume":"43","author":"Bernal","year":"2015","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_35","first-page":"130","article-title":"Automated polyp detection in colonoscopy videos using shape and context information","volume":"35","author":"Tajbakhsh","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s11548-013-0926-3","article-title":"Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer","volume":"9","author":"Silva","year":"2014","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_37","unstructured":"Loshchilov, I., and Hutter, F. (2019, January 6\u20139). Decoupled weight decay regularization. Proceedings of the International Conference on Learning Representations (ICLR), New Orleans, LA, USA."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Patel, K., Bur, A.M., and Wang, G. (2021, January 26\u201328). Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentation. Proceedings of the 2021 18th Conference on Robots and Vision (CRV), Burnaby, BC, Canada.","DOI":"10.1109\/CRV52889.2021.00032"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hu, B.C., Ji, G.P., Shao, D., and Fan, D.P. (2025). PraNet-V2: Dual-supervised reverse attention for medical image segmentation. arXiv.","DOI":"10.26599\/CVM.2025.9450510"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/9\/2717\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T11:59:10Z","timestamp":1777377550000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/9\/2717"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,28]]},"references-count":39,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["s26092717"],"URL":"https:\/\/doi.org\/10.3390\/s26092717","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,28]]}}}