{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T19:45:43Z","timestamp":1782935143848,"version":"3.54.5"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Postgraduate Education Reform and Quality Improvement Project of Henan Province","award":["YJS2026AL151"],"award-info":[{"award-number":["YJS2026AL151"]}]},{"name":"Interdisciplinary Sciences Project, Nanyang Institute of Technology"},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan","doi-asserted-by":"crossref","award":["252300421874"],"award-info":[{"award-number":["252300421874"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Major Science and Technology Project of Nanyang","award":["25ZDZX007"],"award-info":[{"award-number":["25ZDZX007"]}]},{"name":"Key Research Programs of Higher Education Institutions in Henan Province","award":["25A520041"],"award-info":[{"award-number":["25A520041"]}]},{"name":"Graduate Education Reform Project of Henan Province","award":["2025SJGLX359Y"],"award-info":[{"award-number":["2025SJGLX359Y"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s11760-026-05484-2","type":"journal-article","created":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T08:30:20Z","timestamp":1781339420000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-perspective feature reorganization network for medical image segmentation"],"prefix":"10.1007","volume":"20","author":[{"given":"Yufeng","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiale","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyu","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,13]]},"reference":[{"key":"5484_CR1","doi-asserted-by":"crossref","unstructured":"Azad, R., Arimond, R., Ehsan Khodapanah Aghdam, A., Kazerouni, D.: Merhof: Dae-former: dual attention-guided efficient transformer for medical image segmentation. In: International workshop on predictive intelligence in medicine, pp. 83\u201395. Springer (2023)","DOI":"10.1007\/978-3-031-46005-0_8"},{"key":"5484_CR2","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: unet-like pure transformer for medical image segmentation. In: European conference on computer vision, pp. 205\u2013218. Springer, (2022)","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"5484_CR3","doi-asserted-by":"crossref","unstructured":"Cao, Y., Xu, J., Lin, S., Wei, F., Hu, H.: Gcnet: non-local networks meet squeeze-excitation networks and beyond, CoRR (2019). arXiv:1904.11492","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"5484_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103280","volume":"97","author":"J Chen","year":"2024","unstructured":"Chen, J., Mei, J., Li, X., Yongyi, L., Qihang, Yu., Wei, Q., Luo, X., Xie, Y., Adeli, E., Wang, Y., Lungren, M.P., Zhang, S., Xing, L., Le, L., Yuille, A., Zhou, Y.: Transunet: rethinking the u-net architecture design for medical image segmentation through the lens of transformers. Med. Image Anal. 97, 103280 (2024)","journal-title":"Med. Image Anal."},{"key":"5484_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119858","volume":"223","author":"Y Ding","year":"2023","unstructured":"Ding, Y., Zhang, Z., Zhao, X., Hong, D., Cai, W., Yang, N., Wang, B.: Multi-scale receptive fields: graph attention neural network for hyperspectral image classification. Expert Syst. Appl. 223, 119858 (2023)","journal-title":"Expert Syst. Appl."},{"key":"5484_CR6","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.neucom.2022.06.031","volume":"501","author":"Y Ding","year":"2022","unstructured":"Ding, Y., Zhang, Z., Zhao, X., Hong, D., Cai, W., Chengguo, Yu., Yang, N., Cai, W.: Multi-feature fusion: graph neural network and cnn combining for hyperspectral image classification. Neurocomputing 501, 246\u2013257 (2022)","journal-title":"Neurocomputing"},{"key":"5484_CR7","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.ins.2022.04.006","volume":"602","author":"Y Ding","year":"2022","unstructured":"Ding, Y., Zhang, Z., Zhao, X., Hong, D., Li, W., Cai, W., Zhan, Y.: Af2gnn: graph convolution with adaptive filters and aggregator fusion for hyperspectral image classification. Inf. Sci. 602, 201\u2013219 (2022)","journal-title":"Inf. Sci."},{"issue":"2","key":"5484_CR8","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1109\/TMI.2020.3035253","volume":"40","author":"G Ran","year":"2021","unstructured":"Ran, G., Wang, G., Song, T., Huang, R., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T., Zhang, S.: Ca-net: comprehensive attention convolutional neural networks for explainable medical image segmentation. IEEE Trans. Med. Imaging 40(2), 699\u2013711 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5484_CR9","doi-asserted-by":"crossref","unstructured":"Han, Z., Jian, M., Wang, G.-G.: Convunext: an efficient convolution neural network for medical image segmentation. Knowl.-Based Syst, vol. 253, p. 109512. (2022)","DOI":"10.1016\/j.knosys.2022.109512"},{"key":"5484_CR10","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141. (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"5484_CR11","doi-asserted-by":"crossref","unstructured":"Jiang, J., Wang, M., Tian, H., Cheng, L., Liu, Y.: Lv-unet: a lightweight and vanilla model for medical image segmentation. In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 4240\u20134246. IEEE (2024)","DOI":"10.1109\/BIBM62325.2024.10822465"},{"issue":"7","key":"5484_CR12","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1109\/TMI.2017.2677499","volume":"36","author":"N Kumar","year":"2017","unstructured":"Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., Sethi, A.: A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans. Med. Imaging 36(7), 1550\u20131560 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5484_CR13","first-page":"1","volume":"71","author":"A Lin","year":"2022","unstructured":"Lin, A., Chen, B., Jiayu, X., Zhang, Z., Guangming, L., Zhang, D.: Ds-transunet: dual swin transformer u-net for medical image segmentation. IEEE Trans. Instrum. Meas. 71, 1\u201315 (2022)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"5484_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102634","volume":"113","author":"X Liu","year":"2025","unstructured":"Liu, X., Gao, P., Tao, Yu., Wang, F., Yuan, R.-Y.: Cswin-unet: transformer unet with cross-shaped windows for medical image segmentation. Information Fusion 113, 102634 (2025)","journal-title":"Information Fusion"},{"key":"5484_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhu, H., Liu, M., Huaiyuan, Yu., Chen, Z., Gao, J.: Rolling-unet: revitalizing mlp\u2019s ability to efficiently extract long-distance dependencies for medical image segmentation. In: Proceedings of the AAAI conference on artificial intelligence 38, 3819\u20133827 (2024)","DOI":"10.1609\/aaai.v38i4.28173"},{"key":"5484_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108010","volume":"170","author":"Y Ma","year":"2024","unstructured":"Ma, Y., Hong, X., Feng, Y., Lin, Z., Li, F., Xin, W., Liu, Q., Zhang, S.: Msdenet: multi-scale detail enhanced network based on human visual system for medical image segmentation. Comput. Biol. Med. 170, 108010 (2024)","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"5484_CR17","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1109\/TMI.2018.2865709","volume":"38","author":"P Naylor","year":"2019","unstructured":"Naylor, P., La\u00e9, M., Reyal, F., Walter, T.: Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Trans. Med. Imaging 38(2), 448\u2013459 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5484_CR18","doi-asserted-by":"crossref","unstructured":"Ba, H., Ngo, Doanh, C., Bui, N.-T., Do-Tran, T.J., Choi: Higda: hierarchical graph of nodes to learn local-to-global topology for semi-supervised domain adaptation. In: Proceedings of the AAAI conference on artificial intelligence, vol. 39, pp. 6191\u20136199. (2025)","DOI":"10.1609\/aaai.v39i6.32662"},{"key":"5484_CR19","doi-asserted-by":"crossref","unstructured":"Ngo, B.H., Do-Tran, N.T., Nguyen, T.N., Jeon, H.G., Choi, T.J.: Learning cnn on vit: a hybrid model to explicitly class-specific boundaries for domain adaptation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 28545\u201328554. (2024)","DOI":"10.1109\/CVPR52733.2024.02697"},{"key":"5484_CR20","unstructured":"Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., Glocker, B., Rueckert, D.: Attention u-net: learning where to look for the pancreas. In Medical Imaging with Deep Learning, (2018)"},{"key":"5484_CR21","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Munir, M., Marculescu, R.: Emcad: efficient multi-scale convolutional attention decoding for medical image segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 11769\u201311779. (2024)","DOI":"10.1109\/CVPR52733.2024.01118"},{"key":"5484_CR22","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. Lect. Notes Comput. Sci. , (2015). (Lecture Notes in Computer Science)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"5484_CR23","doi-asserted-by":"crossref","unstructured":"Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. , 1455\u20131462 (2016)","DOI":"10.1109\/TBME.2015.2496264"},{"issue":"4","key":"5484_CR24","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"J Staal","year":"2004","unstructured":"Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501\u2013509 (2004)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5484_CR25","unstructured":"Su, X., Xue, S., Liu, F., Wu, J., Yang, J., Zhou, C., Hu, W., Paris, C., Nepal, S., Jin, D., Sheng, Q.Z., Yu, P.S.: A comprehensive survey on community detection with deep learning. IEEE Trans. Neural Netw. Learn. Syst , 1\u201321 (2022)"},{"key":"5484_CR26","doi-asserted-by":"crossref","unstructured":"Tang, F., Ding, J., Quan, Q., Wang, L., Ning, C., Zhou, S. K: Cmunext: An efficient medical image segmentation network based on large kernel and skip fusion. In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135. IEEE, (2024)","DOI":"10.1109\/ISBI56570.2024.10635609"},{"issue":"2","key":"5484_CR27","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1109\/TMI.2021.3117495","volume":"41","author":"F Uslu","year":"2022","unstructured":"Uslu, F., Varela, M., Boniface, G., Mahenthran, T., Chubb, H., Bharath, A.A.: La-net: a multi-task deep network for the segmentation of the left atrium. IEEE Trans. Med. Imaging 41(2), 456\u2013464 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5484_CR28","doi-asserted-by":"crossref","unstructured":"Wang, H., Cao, P., Wang, J., Zaiane, O. R.: Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI conference on artificial intelligence, vol.36, pp. 2441\u20132449, (2022)","DOI":"10.1609\/aaai.v36i3.20144"},{"key":"5484_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 11534\u201311542. (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"5484_CR30","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. CVPR , (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"5484_CR31","doi-asserted-by":"crossref","unstructured":"Xiao, X., Lian, S., Luo, Z., Li, S.: Weighted res-unet for high-quality retina vessel segmentation. In: 9th International Conference on Information Technology in Medicine and Education (ITME), (2018)","DOI":"10.1109\/ITME.2018.00080"},{"key":"5484_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124935","volume":"256","author":"Y Xiao","year":"2024","unstructured":"Xiao, Y., Zhao, J., Yanze, Yu., Ding, X., Liu, S., Bao, W., Wen, S., Zhou, X.: Simplecnn-unet: an optic disc image segmentation network based on efficient small-kernel convolutions. Expert Syst. Appl. 256, 124935 (2024)","journal-title":"Expert Syst. Appl."},{"key":"5484_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106626","volume":"154","author":"X Qing","year":"2023","unstructured":"Qing, X., Ma, Z., Duan, W., et al.: Dcsau-net: a deeper and more compact split-attention u-net for medical image segmentation. Comput. Biol. Med. 154, 106626 (2023)","journal-title":"Comput. Biol. Med."},{"key":"5484_CR34","doi-asserted-by":"crossref","unstructured":"You, Z., Yu, H., Xiao, Z., Peng, T., Wei, Y.: Cas-unet: a retinal segmentation method based on attention. Electronics 12(15), (2023)","DOI":"10.3390\/electronics12153359"},{"key":"5484_CR35","doi-asserted-by":"crossref","unstructured":"Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017)","DOI":"10.1109\/CVPR.2017.75"},{"key":"5484_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.110536","volume":"150","author":"J Zhang","year":"2025","unstructured":"Zhang, J., Li, D., Zeng, Z., Zhang, R., Wang, J.: Dual-branch crack segmentation network with multi-shape kernel based on convolutional neural network and mamba. Eng. Appl. Artif. Intell. 150, 110536 (2025)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"5484_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2025.111723","volume":"167","author":"J Zhang","year":"2025","unstructured":"Zhang, J., Zhang, S., Li, D., Wang, J., Wang, J.: Crack segmentation network via difference convolution-based encoder and hybrid cnn-mamba multi-scale attention. Pattern Recogn. 167, 111723 (2025)","journal-title":"Pattern Recogn."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05484-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-026-05484-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05484-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T17:55:48Z","timestamp":1782928548000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-026-05484-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":37,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["5484"],"URL":"https:\/\/doi.org\/10.1007\/s11760-026-05484-2","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"4 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"412"}}