{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T10:25:20Z","timestamp":1758450320735,"version":"3.44.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032049469"},{"type":"electronic","value":"9783032049476"}],"license":[{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-04947-6_16","type":"book-chapter","created":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:32:30Z","timestamp":1758389550000},"page":"161-171","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EFMS-Net: Efficient Frequency-Enhanced Multi-scale Network for\u00a0Ischemic Stroke Segmentation"],"prefix":"10.1007","author":[{"given":"Jie","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaowei","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuwei","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhibin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lianfen","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihong","family":"Zhan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"key":"16_CR1","unstructured":"Hui, C., Tadi, P., Suheb, M.Z., Patti, L.: Ischemic stroke. StatPearls [Internet] (2024)"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"He, Y., Nath, V., Yang, D., Tang, Y., Myronenko, A., Xu, D.: Swinunetr-v2: Stronger swin transformers with stagewise convolutions for 3d medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 416\u2013426. Springer (2023)","DOI":"10.1007\/978-3-031-43901-8_40"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"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\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Roy, S., Koehler, G., Ulrich, C., Baumgartner, M., Petersen, J., Isensee, F., Jaeger, P.F., Maier-Hein, K.H.: Mednext: Transformer-driven scaling of convnets for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 405\u2013415. Springer (2023)","DOI":"10.1007\/978-3-031-43901-8_39"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Shaker, A.M., Maaz, M., Rasheed, H., Khan, S., Yang, M.H., Khan, F.S.: UNETR++: Delving into efficient and accurate 3D medical image segmentation. IEEE Transactions on Medical Imaging (2024)","DOI":"10.1109\/TMI.2024.3398728"},{"issue":"1","key":"16_CR6","doi-asserted-by":"publisher","first-page":"7868","DOI":"10.1038\/s41598-022-11852-y","volume":"12","author":"H Liu","year":"2022","unstructured":"Liu, H., Feng, Y., Xu, H., Liang, S., Liang, H., Li, S., Zhu, J., Yang, S., Li, F.: MEA-Net: Multilayer edge attention network for medical image segmentation. Sci. Rep. 12(1), 7868 (2022)","journal-title":"Sci. Rep."},{"key":"16_CR7","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.neucom.2021.10.017","volume":"468","author":"R Gu","year":"2022","unstructured":"Gu, R., Wang, L., Zhang, L.: DE-Net: A deep edge network with boundary information for automatic skin lesion segmentation. Neurocomputing 468, 71\u201384 (2022)","journal-title":"Neurocomputing"},{"key":"16_CR8","unstructured":"Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023)"},{"key":"16_CR9","unstructured":"Ma, J., Li, F., Wang, B.: U-mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv:2401.04722 (2024)"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, J., Chen, D., Wu, J.: LKM-UNet: Large kernel vision mamba unet for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 360\u2013370. Springer (2024)","DOI":"10.1007\/978-3-031-72111-3_34"},{"key":"16_CR11","unstructured":"Liao, W., Zhu, Y., Wang, X., Pan, C., Wang, Y., Ma, L.: Lightm-unet: Mamba assists in lightweight unet for medical image segmentation. arXiv preprint arXiv:2403.05246 (2024)"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Qin, Z., Zhang, P., Wu, F., Li, X.: Fcanet: Frequency channel attention networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 783\u2013792 (2021)","DOI":"10.1109\/ICCV48922.2021.00082"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"She, D., Zhang, Y., Zhang, Z., Li, H., Yan, Z., Sun, X.: EoFormer: Edge-Oriented Transformer for Brain Tumor Segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 333\u2013343. Springer (2023)","DOI":"10.1007\/978-3-031-43901-8_32"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Wu, J., Ji, W., Fu, H., Xu, M., Jin, Y., Xu, Y.: Medsegdiff-v2: Diffusion-based medical image segmentation with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence 38(6), 6030\u20136038 (2024)","DOI":"10.1609\/aaai.v38i6.28418"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015. pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"16_CR17","unstructured":"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)"},{"issue":"1","key":"16_CR18","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/T-C.1974.223784","volume":"100","author":"N Ahmed","year":"2006","unstructured":"Ahmed, N., Natarajan, T., Rao, K.R.: Discrete cosine transform. IEEE Trans. Comput. 100(1), 90\u201393 (2006)","journal-title":"IEEE Trans. Comput."},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6848\u20136856 (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Marr, D., Hildreth, E.: Theory of edge detection. Proceedings of the Royal Society of London. Series B. Biological Sciences 207(1167), 187\u2013217 (1980)","DOI":"10.1098\/rspb.1980.0020"},{"issue":"1","key":"16_CR21","doi-asserted-by":"publisher","first-page":"762","DOI":"10.1038\/s41597-022-01875-5","volume":"9","author":"MR Hernandez Petzsche","year":"2022","unstructured":"Hernandez Petzsche, M.R., de la Rosa, E., Hanning, U., Wiest, R., Valenzuela, W., Reyes, M., Meyer, M., Liew, S.L., Kofler, F., Ezhov, I., Robben, D.: ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset. Scientific Data 9(1), 762 (2022)","journal-title":"Scientific Data"},{"key":"16_CR22","unstructured":"The ISLES Challenge 2018 website. Available: https:\/\/www.isles-challenge.org\/ISLES2018\/"},{"issue":"1","key":"16_CR23","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1038\/s41597-022-01401-7","volume":"9","author":"SL Liew","year":"2022","unstructured":"Liew, S.L., Lo, B.P., Donnelly, M.R., Zavaliangos-Petropulu, A., Jeong, J.N., Barisano, G., Hutton, A., Simon, J.P., Juliano, J.M., Suri, A., Wang, Z.: A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms. Scientific Data 9(1), 320 (2022)","journal-title":"Scientific Data"},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Yushkevich, P.A., Gao, Y., Gerig, G.: ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images. In: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. pp. 3342\u20133345 (2016)","DOI":"10.1109\/EMBC.2016.7591443"},{"issue":"2","key":"16_CR25","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"16_CR26","unstructured":"Lee, H.H., Bao, S., Huo, Y., Landman, B.A.: 3D UX-Net: A large kernel volumetric convnet modernizing hierarchical transformer for medical image segmentation. arXiv preprint arXiv:2209.15076 (2022)"},{"key":"16_CR27","doi-asserted-by":"publisher","first-page":"4036","DOI":"10.1109\/TIP.2023.3293771","volume":"32","author":"HY Zhou","year":"2023","unstructured":"Zhou, H.Y., Guo, J., Zhang, Y., Han, X., Yu, L., Wang, L., Yu, Y.: nnFormer: Volumetric medical image segmentation via a 3D transformer. IEEE Trans. Image Process. 32, 4036\u20134045 (2023)","journal-title":"IEEE Trans. Image Process."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04947-6_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:32:38Z","timestamp":1758389558000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04947-6_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,21]]},"ISBN":["9783032049469","9783032049476"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04947-6_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,21]]},"assertion":[{"value":"21 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}