{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T11:17:04Z","timestamp":1786619824451,"version":"3.56.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Natural Science Foundation of Chongqing, China","award":["Grant No: cstc2021jcyj-msxmX0605"],"award-info":[{"award-number":["Grant No: cstc2021jcyj-msxmX0605"]}]},{"name":"Science and Technology Foundation of Chongqing Education Commission","award":["Grant Nos. KJQN202001137"],"award-info":[{"award-number":["Grant Nos. KJQN202001137"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s11760-022-02388-9","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T19:02:36Z","timestamp":1668625356000},"page":"1775-1783","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["DS-UNeXt: depthwise separable convolution network with large convolutional kernel for medical image segmentation"],"prefix":"10.1007","volume":"17","author":[{"given":"Tongyuan","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangxia","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linfeng","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"key":"2388_CR1","doi-asserted-by":"crossref","unstructured":"Sun, S., Liu, Y., Bai, N., et al.: Attentionanatomy: A unified framework for whole-body organs at risk segmentation using multiple partially annotated datasets. In: Proceedings of the IEEE International Symposium on Biomedical Imaging, pp. 1\u20135 (2020)","DOI":"10.1109\/ISBI45749.2020.9098588"},{"key":"2388_CR2","doi-asserted-by":"crossref","unstructured":"Tang, H., Zhang, C., Xie, X.: Automatic pulmonary lobe segmentation using deep learning. In: Proceedings of the IEEE International Symposium on Biomedical Imaging, pp. 1225\u20131228 (2019)","DOI":"10.1109\/ISBI.2019.8759468"},{"key":"2388_CR3","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2388_CR4","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., et al.: nnUNet: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"2388_CR5","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s10462-020-09854-1","volume":"54","author":"S Asgari Taghanaki","year":"2021","unstructured":"Asgari Taghanaki, S., Abhishek, K., Cohen, J.P., et al.: Deep semantic segmentation of natural and medical images: a review. Artif. Intell. Rev. 54, 137\u2013178 (2021)","journal-title":"Artif. Intell. Rev."},{"key":"2388_CR6","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., et al.: 3D UNet: learning dense volumetric segmentation from sparse annotation. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 424\u2013432 (2016)","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"2388_CR7","doi-asserted-by":"crossref","unstructured":"Xiao, X., Lian, S., Luo, Z., et al.: Weighted res-unet for high-quality retina vessel segmentation. In: Proceedings of the International Conference on Information Technology in Medicine and Education, pp..327\u2013331 (2018)","DOI":"10.1109\/ITME.2018.00080"},{"key":"2388_CR8","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., et al.: UNet++: a nested UNet architecture for medical image segmentation. In: Proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3\u201311 (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"2388_CR9","unstructured":"Oktay O., Schlemper, J., Folgoc, L.L., et al.: Attention UNet: learning where to look for the pancreas. arXiv:1804.03999 (2018)"},{"key":"2388_CR10","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, L., Tong, R., et al.: UNet 3+: a full-scale connected UNet for medical image segmentation. In: Proceedings of the ICASSP 2020\u20132020 IEEE International Conference on Acoustics, pp. 1055\u20131059 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"2388_CR11","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1049\/cit2.12061","volume":"6","author":"S Karimi Jafarbigloo","year":"2021","unstructured":"Karimi Jafarbigloo, S., Danyali, H.: Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification. CAAI Trans. Intell. Technol. 6, 426\u2013439 (2021)","journal-title":"CAAI Trans. Intell. Technol."},{"key":"2388_CR12","doi-asserted-by":"publisher","DOI":"10.1049\/cit2.12072","author":"Y Jia","year":"2022","unstructured":"Jia, Y., Wang, H., Chen, W., et al.: An attention-based cascade R-CNN model for sternum fracture detection in X-ray images. CAAI Trans. Intell. Technol. (2022). https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1049\/cit2.12072","journal-title":"CAAI Trans. Intell. Technol."},{"key":"2388_CR13","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 4\u20139 (2017)"},{"key":"2388_CR14","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., et al.: An image is worth 16\u2009\u00d7\u200916 words: transformers for image recognition at scale. arXiv:2010.11929 (2020)"},{"key":"2388_CR15","unstructured":"Chen, J., Lu, Y., Yu, Q., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv:2102.04306 (2021)"},{"key":"2388_CR16","unstructured":"Zhou, H. Y., Guo, J., Zhang, Y., et al.: nnformer: interleaved transformer for volumetric segmentation. arXiv:2109.03201 (2021)"},{"key":"2388_CR17","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Tang, Y., Nath, V., et al.: Unetr: transformers for 3d medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 574\u2013584 (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"2388_CR18","unstructured":"Jun, E., Jeong, S, Heo, D.W., et al.: Medical transformer: universal brain encoder for 3D MRI analysis. arXiv:2104.13633 (2021)"},{"key":"2388_CR19","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1109\/TMI.2021.3108910","volume":"41","author":"S He","year":"2021","unstructured":"He, S., Grant, P.E., Ou, Y.: Global-local transformer for brain age estimation. IEEE Trans. Med. Imaging 41, 213\u2013224 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2388_CR20","doi-asserted-by":"crossref","unstructured":"Costa, G.S.S., Paiva, A.C., Junior, G.B., et al.: COVID-19 automatic diagnosis with CT images using the novel transformer architecture. In: Anais do XXI simp\u00f3sio brasileiro de computa\u00e7\u00e3o aplicada \u00e0 sa\u00fade, pp. 293\u2013301 (2021)","DOI":"10.5753\/sbcas.2021.16073"},{"key":"2388_CR21","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., et al.: 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":"2388_CR22","unstructured":"Cao, H., Wang, Y., Chen, J., et al.: Swin-Unet: Unet-like pure transformer for medical image segmentation. arXiv:2105.05537 (2021)"},{"key":"2388_CR23","first-page":"1","volume":"71","author":"A Lin","year":"2022","unstructured":"Lin, A., Chen, B., Xu, J., et al.: 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":"2388_CR24","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., et al.: A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976\u201311986 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"2388_CR25","unstructured":"Howard, A.G., Zhu, M., Chen, B., et al.: MobileNets: efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 (2017)"},{"key":"2388_CR26","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1109\/TMI.2002.808355","volume":"22","author":"A Tsai","year":"2003","unstructured":"Tsai, A., Yezzi, A., Wells, W., et al.: A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans. Med. Imaging 22, 137\u2013154 (2003)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2388_CR27","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1109\/42.650883","volume":"16","author":"K Held","year":"1997","unstructured":"Held, K., Kops, E.R., Krause, B.J., et al.: Markov random field segmentation of brain MR images. IEEE Trans. Med. Imaging 16, 878\u2013886 (1997)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"2388_CR28","first-page":"22","volume":"2","author":"DD Patil","year":"2013","unstructured":"Patil, D.D., Deore, S.G.: Medical image segmentation: a review. Int. J. Comput. Sci. Mobile Comput. 2(1), 22\u201327 (2013)","journal-title":"Int. J. Comput. Sci. Mobile Comput."},{"key":"2388_CR29","doi-asserted-by":"crossref","unstructured":"Cao, L., Liang Y., Lv, W., et al.: Relating brain structure images to personality characteristics using 3D convolution neural network. In: Proceedings of the CAAI Transactions on Intelligence Technology, vol. 6(3), pp. 338\u2013346 (2021)","DOI":"10.1049\/cit2.12021"},{"key":"2388_CR30","doi-asserted-by":"publisher","first-page":"2682","DOI":"10.1049\/iet-ipr.2019.1527","volume":"14","author":"Y Cao","year":"2020","unstructured":"Cao, Y., Liu, S., Peng, Y., et al.: DenseUNet: densely connected UNet for electron microscopy image segmentation. IET Image Proc. 14, 2682\u20132689 (2020)","journal-title":"IET Image Proc."},{"issue":"3","key":"2388_CR31","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1002\/ima.22428","volume":"30","author":"H Zhao","year":"2020","unstructured":"Zhao, H., Qiu, X., Lu, W., Huang, H., et al.: High-quality retinal vessel segmentation using generative adversarial network with a large receptive field. Int. J. Imaging Syst. Technol. 30(3), 828\u2013842 (2020)","journal-title":"Int. J. Imaging Syst. Technol."},{"issue":"11","key":"2388_CR32","doi-asserted-by":"publisher","first-page":"2453","DOI":"10.1109\/TMI.2018.2835303","volume":"37","author":"L Chen","year":"2018","unstructured":"Chen, L., Bentley, P., Mori, K., et al.: DRINet for medical image segmentation. IEEE Trans. Med. Imaging 37(11), 2453\u20132462 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2388_CR33","doi-asserted-by":"crossref","unstructured":"Milletari, F., Nassir N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: Proceedings of the 2016 Fourth International Conference on 3D Vision, pp. 565\u2013571 (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"2388_CR34","unstructured":"Devlin, J., Chang, M.W., Lee, K., et al.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018)"},{"key":"2388_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Du, T., Sun, Y., et al.: Form 10-q itemization. In: Proceedings of the 30th ACM International Conference on Information Knowledge Management, pp. 4817\u20134822 (2021)","DOI":"10.1145\/3459637.3481989"},{"key":"2388_CR36","unstructured":"Chang, Y., Menghan, H., Guangtao, Z., et al.: Transclaw UNet: claw UNet with transformers for medical image segmentation. arXiv:2107.05188 (2021)"},{"key":"2388_CR37","unstructured":"Sha, Y., Zhang, Y., Ji, X., et al.: Transformer-UNet: raw image processing with UNet. arXiv:2109.08417 (2021)"},{"key":"2388_CR38","doi-asserted-by":"crossref","unstructured":"Gao, Y., Zhou, M., Metaxas, D.N.: UTNet: a hybrid transformer architecture for medical image segmentation. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 61\u201371 (2021)","DOI":"10.1007\/978-3-030-87199-4_6"},{"key":"2388_CR39","doi-asserted-by":"crossref","unstructured":"Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., et al.: Medical transformer: gated axial-attention for medical image segmentation. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 36\u201346 (2021)","DOI":"10.1007\/978-3-030-87193-2_4"},{"key":"2388_CR40","doi-asserted-by":"crossref","unstructured":"Xie, Y., Zhang, J., Shen, C., et al.: Cotr: efficiently bridging cnn and transformer for 3d medical image segmentation. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, pp. 171\u2013180 (2021)","DOI":"10.1007\/978-3-030-87199-4_16"},{"key":"2388_CR41","doi-asserted-by":"crossref","unstructured":"Tang, Y., Yang, D., Li, W., et al.: A. Self-supervised pre-training of swin transformers for 3d medical image analysis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20730\u201320740 (2022)","DOI":"10.1109\/CVPR52688.2022.02007"},{"key":"2388_CR42","unstructured":"Ba, J.L., Kiros, J.R., Hinton, G.E. Layer normalization. arXiv:1607.06450 (2016)"},{"key":"2388_CR43","unstructured":"Ioffe, S.: Batch renormalization: towards reducing minibatch dependence in batch-normalized models. In: Proceedings of the Advances in Neural Information Processing Systems, p. 30 (2017)"},{"key":"2388_CR44","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning, pp. 21\u201324 (2010)"},{"key":"2388_CR45","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., et al.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40, 834\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2388_CR46","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., et al.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1492\u20131500 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"2388_CR47","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., et al.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"2388_CR48","unstructured":"Hendrycks, D., Kevin, G.: Gaussian error linear units (gelus). arXiv:1606.08415 (2016)"},{"key":"2388_CR49","doi-asserted-by":"crossref","unstructured":"Fu, S., Lu, Y., Wang, Y., et al.: Domain adaptive relational reasoning for 3d multi-organ segmentation. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 656\u2013666 (2020)","DOI":"10.1007\/978-3-030-59710-8_64"},{"key":"2388_CR50","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard, O., Lalande, A., Zotti, C., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37, 2514\u20132525 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2388_CR51","unstructured":"Loshchilov, I., Frank, H.: Decoupled weight decay regularization. arXiv:1711.05101 (2017)"},{"key":"2388_CR52","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","volume":"53","author":"J Schlemper","year":"2019","unstructured":"Schlemper, J., Oktay, O., Schaap, M., et al.: Attention gated networks: learning to leverage salient regions in medical images. Med. Image Anal. 53, 197\u2013207 (2019)","journal-title":"Med. Image Anal."},{"key":"2388_CR53","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., et al.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02388-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/article\/10.1007\/s11760-022-02388-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02388-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T04:10:33Z","timestamp":1684383033000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/10.1007\/s11760-022-02388-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,16]]},"references-count":53,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["2388"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/s11760-022-02388-9","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,16]]},"assertion":[{"value":"19 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2022","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Synapse for multiorgan CT segmentation dataset and ACDC dataset belongs to public datasets. The patients involved in the dataset have obtained ethical approval. User can download relevant data for free for research and publish relevant articles. Our study is based on open-source data, so there are no ethical issues.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}