{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T18:15:40Z","timestamp":1758478540441,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031721106"},{"type":"electronic","value":"9783031721113"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72111-3_31","type":"book-chapter","created":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T21:01:34Z","timestamp":1728162094000},"page":"328-338","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["$$\\text {I}^2$$Net: Exploiting Misaligned Contexts Orthogonally with\u00a0Implicit-Parameterized Implicit Functions for\u00a0Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Jiahao","family":"Yu","sequence":"first","affiliation":[]},{"given":"Fan","family":"Duan","sequence":"additional","affiliation":[]},{"given":"Li","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,6]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","first-page":"108205","DOI":"10.1109\/ACCESS.2022.3211501","volume":"10","author":"R Azad","year":"2022","unstructured":"Azad, R., Al-Antary, M.T., Heidari, M., Merhof, D.: Transnorm: transformer provides a strong spatial normalization mechanism for a deep segmentation model. IEEE Access 10, 108205\u2013108215 (2022)","journal-title":"IEEE Access"},{"key":"31_CR2","doi-asserted-by":"publisher","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, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-25066-8_9","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"31_CR3","unstructured":"Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"31_CR4","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)"},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213\u20133223 (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"31_CR6","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","volume":"162","author":"FI Diakogiannis","year":"2020","unstructured":"Diakogiannis, F.I., Waldner, F., Caccetta, P., Wu, C.: Resunet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS J. Photogramm. Remote. Sens. 162, 94\u2013114 (2020)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Ji, G.P., Zhou, T., Chen, G., Fu, H., Shen, J., Shao, L.: Pranet: parallel reverse attention network for polyp segmentation. In: Medical Image Computing and Computer-Assisted Intervention, pp. 263\u2013273 (2020)","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"Fu, S., et al.: Domain adaptive relational reasoning for 3d multi-organ segmentation. In: Medical Image Computing and Computer Assisted Intervention, pp. 656\u2013666. Springer (2020)","DOI":"10.1007\/978-3-030-59710-8_64"},{"issue":"10","key":"31_CR9","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu, Z., et al.: Ce-net: context encoder network for 2d medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281\u20132292 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"31_CR10","doi-asserted-by":"publisher","first-page":"7436","DOI":"10.1109\/TPAMI.2021.3117837","volume":"44","author":"Y Han","year":"2022","unstructured":"Han, Y., Huang, G., Song, S., Yang, L., Wang, H., Wang, Y.: Dynamic neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(11), 7436\u20137456 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"31_CR11","doi-asserted-by":"publisher","unstructured":"Hu, H., et al.: Learning implicit feature alignment function for semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), vol. 13689, pp. 487\u2013505 (2022). https:\/\/doi.org\/10.1007\/978-3-031-19818-2_28","DOI":"10.1007\/978-3-031-19818-2_28"},{"issue":"1","key":"31_CR12","first-page":"550","volume":"44","author":"Z Huang","year":"2022","unstructured":"Huang, Z., Wei, Y., Wang, X., Liu, W., Huang, T.S., Shi, H.: Alignseg: feature-aligned segmentation networks. IEEE Trans. Pattern Anal. Mach. Intell. 44(1), 550\u2013557 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"31_CR13","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","volume":"121","author":"N Ibtehaz","year":"2020","unstructured":"Ibtehaz, N., Rahman, M.S.: Multiresunet: rethinking the u-net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74\u201387 (2020)","journal-title":"Neural Netw."},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Ji, Y., Zhang, R., Wang, H., Li, Z., Wu, L., Zhang, S., Luo, P.: Multi-compound transformer for accurate biomedical image segmentation. In: Medical Image Computing and Computer Assisted Intervention, pp. 326\u2013336 (2021)","DOI":"10.1007\/978-3-030-87193-2_31"},{"key":"31_CR15","doi-asserted-by":"publisher","unstructured":"Khan, M.O., Fang, Y.: Implicit neural representations for medical imaging segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 433\u2013443. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19818-2_28","DOI":"10.1007\/978-3-031-19818-2_28"},{"key":"31_CR16","unstructured":"Landman, B., Xu, Z., Igelsias, J.E., Styner, M., Langerak, T., Klein, A.: Segmentation outside the cranial vault challenge. In: MICCAI: Multi Atlas Labeling Beyond Cranial Vault-Workshop Challenge (2015)"},{"key":"31_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1007\/978-3-030-58452-8_45","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Li","year":"2020","unstructured":"Li, X., You, A., Zhu, Z., Zhao, H., Yang, M., Yang, K., Tan, S., Tong, Y.: Semantic flow for fast and accurate scene parsing. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 775\u2013793. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_45"},{"key":"31_CR18","doi-asserted-by":"crossref","unstructured":"Lin, A., Xu, J., Li, J., Lu, G.: Contrans: improving transformer with convolutional attention for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 297\u2013307 (2022)","DOI":"10.1007\/978-3-031-16443-9_29"},{"key":"31_CR19","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"31_CR20","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1520\u20131528 (2015)","DOI":"10.1109\/ICCV.2015.178"},{"key":"31_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"31_CR22","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., Heinrich, M., Kainz, B., Glocker, B., Rueckert, D.: Attention gated networks: learning to leverage salient regions in medical images. Med. Image Anal. 53, 197\u2013207 (2019)","journal-title":"Med. Image Anal."},{"key":"31_CR23","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.media.2016.08.008","volume":"35","author":"K Sirinukunwattana","year":"2017","unstructured":"Sirinukunwattana, K., Pluim, J.P., Chen, H., Qi, X., Heng, P.A., Guo, Y.B., Wang, L.Y., Matuszewski, B.J., Bruni, E., Sanchez, U., et al.: Gland segmentation in colon histology images: the glas challenge contest. Med. Image Anal. 35, 489\u2013502 (2017)","journal-title":"Med. Image Anal."},{"key":"31_CR24","doi-asserted-by":"crossref","unstructured":"Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., Patel, V.M.: Medical transformer: gated axial-attention for medical image segmentation. In: Medical Image Computing and Computer Assisted Intervention, pp. 36\u201346 (2021)","DOI":"10.1007\/978-3-030-87193-2_4"},{"key":"31_CR25","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.\u00a036, pp. 2441\u20132449 (2022)","DOI":"10.1609\/aaai.v36i3.20144"},{"key":"31_CR26","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: Mixed transformer u-net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2390\u20132394. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9746172"},{"issue":"9","key":"31_CR27","doi-asserted-by":"crossref","first-page":"4674","DOI":"10.1109\/TPAMI.2021.3072422","volume":"44","author":"J Wang","year":"2022","unstructured":"Wang, J., Chen, K., Xu, R., Liu, Z., Loy, C.C., Lin, D.: CARAFE++: unified content-aware reassembly of features. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 4674\u20134687 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"31_CR28","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: simple and efficient design for semantic segmentation with transformers. Adv. Neural. Inf. Process. Syst. 34, 12077\u201312090 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"31_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, H., Hu, Q.: Transfuse: fusing transformers and cnns for medical image segmentation. In: International Conference on Medical Image Computing and Computer Assisted Intervention, pp. 14\u201324 (2021)","DOI":"10.1007\/978-3-030-87193-2_2"},{"key":"31_CR30","doi-asserted-by":"crossref","unstructured":"Zheng, S., et\u00a0al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6881\u20136890 (2021)","DOI":"10.1109\/CVPR46437.2021.00681"},{"issue":"6","key":"31_CR31","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2019","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856\u20131867 (2019)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72111-3_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T21:04:51Z","timestamp":1728162291000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72111-3_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721106","9783031721113"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72111-3_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"6 October 2024","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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}