{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T03:25:44Z","timestamp":1783481144967,"version":"3.55.0"},"reference-count":28,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,12]],"date-time":"2025-01-12T00:00:00Z","timestamp":1736640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The colonoscopy procedure heavily relies on the operator\u2019s expertise, underscoring the importance of automated polyp segmentation techniques in enhancing the efficiency and accuracy of colorectal cancer diagnosis. Nevertheless, achieving precise segmentation remains a significant challenge due to the high visual similarity between polyps and their backgrounds, blurred boundaries, and complex localization. To address these challenges, a Multi-scale Selective Edge-Aware Network has been proposed to facilitate polyp segmentation. The model consists of three key components: (1) an Edge Feature Extractor (EFE) that captures polyp edge features with precision during the initial encoding phase, (2) the Cross-layer Context Fusion (CCF) block designed to extract and integrate multi-scale contextual information from diverse receptive fields, and (3) the Selective Edge Aware (SEA) module that enhances sensitivity to high-frequency edge details during the decoding phase, thereby improving edge preservation and segmentation accuracy. The effectiveness of our model has been rigorously validated on the Kvasir-SEG, Kvasir-Sessile, and BKAI datasets, achieving mean Dice scores of 91.92%, 82.10%, and 92.24%, respectively, on the test sets.<\/jats:p>","DOI":"10.3390\/a18010042","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T06:42:15Z","timestamp":1736750535000},"page":"42","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MSEANet: Multi-Scale Selective Edge Aware Network for Polyp Segmentation"],"prefix":"10.3390","volume":"18","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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1647-1769","authenticated-orcid":false,"given":"Ming","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Internet of Things Engineering, Wuxi University, Wuxi 214105, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s10462-023-10621-1","article-title":"A systematic review of deep learning based image segmentation to detect polyp","volume":"57","author":"Gupta","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"411","DOI":"10.5217\/ir.2017.15.3.411","article-title":"Miss rate of colorectal neoplastic polyps and risk factors for missed polyps in consecutive colonoscopies","volume":"15","author":"Kim","year":"2017","journal-title":"Intest. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/S0016-5085(97)70214-2","article-title":"Colonoscopic miss rates of adenomas determined by back-to-back colonoscopies","volume":"112","author":"Rex","year":"1997","journal-title":"Gastroenterology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1007\/s42979-021-00704-7","article-title":"Biomedical image segmentation: A survey","volume":"2","author":"Alzahrani","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_5","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\u2013MICCAI 2015: 18th International Conference, Munich, Germany. Proceedings, Part III 18."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/j.ultrasmedbio.2008.10.014","article-title":"Statistical region-based segmentation of ultrasound images","volume":"35","author":"Slabaugh","year":"2009","journal-title":"Ultrasound Med. Biol."},{"key":"ref_7","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., and Liang, J. (2018, January 20). Unet++: A nested u-net architecture for medical image segmentation. Proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain. Proceedings 4."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., Johansen, D., De Lange, T., Halvorsen, P., and Johansen, H.D. (2019, January 9\u201311). Resunet++: An advanced architecture for medical image segmentation. Proceedings of the 2019 IEEE International Symposium on Multimedia (ISM), San Diego, CA, USA.","DOI":"10.1109\/ISM46123.2019.00049"},{"key":"ref_9","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 International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Peru.","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3008","DOI":"10.1109\/TMI.2020.2983721","article-title":"CPFNet: Context pyramid fusion network for medical image segmentation","volume":"39","author":"Feng","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tomar, N.K., Jha, D., Bagci, U., and Ali, S. (2022, January 18\u201322). TGANet: Text-guided attention for improved polyp segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Singapore.","DOI":"10.1007\/978-3-031-16437-8_15"},{"key":"ref_12","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., De Lange, T., Johansen, D., and Johansen, H.D. (2020, January 5\u20138). Kvasir-seg: A segmented polyp dataset. Proceedings of the MultiMedia Modeling: 26th International Conference, MMM 2020, Daejeon, Republic of Korea. Proceedings, Part II 26."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2029","DOI":"10.1109\/JBHI.2021.3049304","article-title":"A comprehensive study on colorectal polyp segmentation with ResUNet++, conditional random field and test-time augmentation","volume":"25","author":"Jha","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ngoc Lan, P., An, N.S., Hang, D.V., Long, D.V., Trung, T.Q., Thuy, N.T., and Sang, D.V. (2021, January 4\u20136). Neounet: Towards accurate colon polyp segmentation and neoplasm detection. Proceedings of the Advances in Visual Computing: 16th International Symposium, ISVC 2021, Virtual. Proceedings, Part II.","DOI":"10.1007\/978-3-030-90436-4_2"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Chen, L.C. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_17","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_18","unstructured":"Vaswani, A. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Feng, R., Lei, B., Wang, W., Chen, T., Chen, J., Chen, D.Z., and Wu, J. (2020, January 3\u20137). SSN: A stair-shape network for real-time polyp segmentation in colonoscopy images. Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA.","DOI":"10.1109\/ISBI45749.2020.9098492"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Han, J., Xu, C., An, Z., Qian, K., Tan, W., Wang, D., and Fang, Q. (2022). PRAPNet: A Parallel Residual Atrous Pyramid Network for Polyp Segmentation. Sensors, 22.","DOI":"10.3390\/s22134658"},{"key":"ref_21","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 16\u201320). Dual attention network for scene segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_23","unstructured":"Lei, M., and Wang, X. (2024). EPPS: Advanced Polyp Segmentation via Edge Information Injection and Selective Feature Decoupling. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"Ce-net: Context encoder network for 2d medical image segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_25","unstructured":"Li, H., Xiong, P., An, J., and Wang, L. (2018). Pyramid attention network for semantic segmentation. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xie, L., Li, C., Wang, Z., Zhang, X., Chen, B., Shen, Q., and Wu, Z. (2023, January 8\u201312). Shisrcnet: Super-resolution and classification network for low-resolution breast cancer histopathology image. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Vancouver, BC, Canada.","DOI":"10.1007\/978-3-031-43904-9_3"},{"key":"ref_27","unstructured":"Kingma, D.P. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"40496","DOI":"10.1109\/ACCESS.2021.3063716","article-title":"Real-time polyp detection, localization and segmentation in colonoscopy using deep learning","volume":"9","author":"Jha","year":"2021","journal-title":"IEEE Access"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/1\/42\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:27:25Z","timestamp":1759919245000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/1\/42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,12]]},"references-count":28,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["a18010042"],"URL":"https:\/\/doi.org\/10.3390\/a18010042","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,12]]}}}