{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T22:59:57Z","timestamp":1793314797909,"version":"4.5.0"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049261","type":"print"},{"value":"9783032049278","type":"electronic"}],"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\/978-3-032-04927-8_13","type":"book-chapter","created":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:09:41Z","timestamp":1758388181000},"page":"130-140","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Co-Seg: Mutual Prompt-Guided Collaborative Learning for\u00a0Tissue and\u00a0Nuclei Segmentation"],"prefix":"10.1007","author":[{"given":"Qing","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenting","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103280","volume":"97","author":"J Chen","year":"2024","unstructured":"Chen, J., et al.: 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":"13_CR2","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1109\/TIP.2023.3237013","volume":"32","author":"S Chen","year":"2023","unstructured":"Chen, S., Ding, C., Liu, M., Cheng, J., Tao, D.: Cpp-net: context-aware polygon proposal network for nucleus segmentation. IEEE Trans. Image Process. 32, 980\u2013994 (2023)","journal-title":"IEEE Trans. Image Process."},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Z., Xu, Q., Liu, X., Yuan, Y.: Un-sam: domain-adaptive self-prompt segmentation for universal nuclei images. Med. Image Anal. p. 103607 (2025)","DOI":"10.1016\/j.media.2025.103607"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Z., et al.: Asi-seg: audio-driven surgical instrument segmentation with surgeon intention understanding. In: IROS, pp. 13773\u201313779. IEEE (2024)","DOI":"10.1109\/IROS58592.2024.10801703"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Wei, Q., Zhu, H., Wang, Y., Qu, L., Shao, W., Zhou, Y.: Unleashing the potential of sam for medical adaptation via hierarchical decoding. In: CVPR, pp. 3511\u20133522 (2024)","DOI":"10.1109\/CVPR52733.2024.00337"},{"key":"13_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101563","volume":"58","author":"S Graham","year":"2019","unstructured":"Graham, S., et al.: Hover-net: simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med. Image Anal. 58, 101563 (2019)","journal-title":"Med. Image Anal."},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Guo, X., Chen, Z., Yuan, Y.: Complementary network with adaptive receptive fields for melanoma segmentation. In: ISBI, pp. 2010\u20132013. IEEE (2020)","DOI":"10.1109\/ISBI45749.2020.9098417"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Hao, Z., Quan, H., Lu, Y.: Emf-former: An efficient and memory-friendly transformer for medical image segmentation. In: MICCAI, pp. 231\u2013241. Springer (2024)","DOI":"10.1007\/978-3-031-72111-3_22"},{"issue":"9","key":"13_CR9","doi-asserted-by":"publisher","first-page":"2763","DOI":"10.1109\/TMI.2023.3264513","volume":"42","author":"A He","year":"2023","unstructured":"He, A., Wang, K., Li, T., Du, C., Xia, S., Fu, H.: H2former: an efficient hierarchical hybrid transformer for medical image segmentation. IEEE Trans. Med. Imaging 42(9), 2763\u20132775 (2023)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"13_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103143","volume":"94","author":"F H\u00f6rst","year":"2024","unstructured":"H\u00f6rst, F., et al.: Cellvit: vision transformers for precise cell segmentation and classification. Med. Image Anal. 94, 103143 (2024)","journal-title":"Med. Image Anal."},{"key":"13_CR11","unstructured":"Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De\u00a0Laroussilhe, Q., Gesmundo, A., Attariyan, M., Gelly, S.: Parameter-efficient transfer learning for nlp. In: ICML. pp. 2790\u20132799. PMLR (2019)"},{"key":"13_CR12","unstructured":"Hu, E.J., et al.: LoRA: low-rank adaptation of large language models. In: ICLR (2022)"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Ibtehaz, N., Kihara, D.: Acc-unet: a completely convolutional unet model for the 2020s. In: MICCAI, pp. 692\u2013702. Springer (2023)","DOI":"10.1007\/978-3-031-43898-1_66"},{"issue":"2","key":"13_CR14","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":"13_CR15","unstructured":"Kirillov, A., et al.: Segment anything. In: ICCV, pp. 4015\u20134026, October 2023"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Li, C., et al.: Flaws can be applause: Unleashing potential of segmenting ambiguous objects in sam. In: NeurIPS, vol.\u00a037, pp. 45578\u201345599 (2024)","DOI":"10.52202\/079017-1449"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Liu, J., et\u00a0al.: Swin-umamba: mamba-based unet with imagenet-based pretraining. In: MICCAI, pp. 615\u2013625. Springer (2024)","DOI":"10.1007\/978-3-031-72114-4_59"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhu, H., Liu, M., Yu, H., Chen, Z., Gao, J.: Rolling-unet: revitalizing mlp\u2019s ability to efficiently extract long-distance dependencies for medical image segmentation. In: AAAI, pp. 3819\u20133827 (2024)","DOI":"10.1609\/aaai.v38i4.28173"},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Lou, Z., et al.: Nusegdg: integration of heterogeneous space and gaussian kernel for domain-generalized nuclei segmentation. Knowledge-Based Systems, p. 113641 (2025)","DOI":"10.1016\/j.knosys.2025.113641"},{"key":"13_CR20","volume-title":"Nusea: Nuclei segmentation with ellipse annotations","author":"Z Meng","year":"2024","unstructured":"Meng, Z., Dong, J., Zhang, B., Li, S., Wu, R., Su, F., Wang, G., Guo, L., Zhao, Z.: Nusea: Nuclei segmentation with ellipse annotations. IEEE J. Biomed, Health Inform (2024)"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Na, S., Guo, Y., Jiang, F., Ma, H., Huang, J.: Segment any cell: a sam-based auto-prompting fine-tuning framework for nuclei segmentation. arXiv preprint arXiv:2401.13220 (2024)","DOI":"10.1109\/TNNLS.2025.3611322"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Nam, J.H., Syazwany, N.S., Kim, S.J., Lee, S.C.: Modality-agnostic domain generalizable medical image segmentation by multi-frequency in multi-scale attention. In: CVPR, pp. 11480\u201311491 (2024)","DOI":"10.1109\/CVPR52733.2024.01091"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Nam, S., Jeong, J., Luna, M., Chikontwe, P., Park, S.H.: Pronet: point refinement using shape-guided offset map for nuclei instance segmentation. In: MICCAI, pp. 528\u2013538. Springer (2023)","DOI":"10.1007\/978-3-031-43907-0_51"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Munir, M., Marculescu, R.: Emcad: Efficient multi-scale convolutional attention decoding for medical image segmentation. In: CVPR, pp. 11769\u201311779 (2024)","DOI":"10.1109\/CVPR52733.2024.01118"},{"key":"13_CR25","unstructured":"Ravi, N., et al.: SAM 2: segment anything in images and videos. In: ICLR (2025)"},{"key":"13_CR26","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\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"13_CR27","unstructured":"Ryali, C., et\u00a0al.: Hiera: A hierarchical vision transformer without the bells-and-whistles. In: ICML, pp. 29441\u201329454. PMLR (2023)"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Schuiveling, M., et al.: A novel dataset for nuclei and tissue segmentation in melanoma with baseline nuclei segmentation and tissue segmentation benchmarks. GigaScience (2025)","DOI":"10.1101\/2024.10.07.24315039"},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Shui, Z., et al.: Unleashing the power of prompt-driven nucleus instance segmentation. In: ECCV, pp. 288\u2013304. Springer (2024)","DOI":"10.1007\/978-3-031-73383-3_17"},{"issue":"1","key":"13_CR30","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1038\/s41592-020-01018-x","volume":"18","author":"C Stringer","year":"2021","unstructured":"Stringer, C., Wang, T., Michaelos, M., Pachitariu, M.: Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18(1), 100\u2013106 (2021)","journal-title":"Nat. Methods"},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"Valanarasu, J.M.J., Patel, V.M.: Unext: Mlp-based rapid medical image segmentation network. In: MICCAI, pp. 23\u201333. Springer (2022)","DOI":"10.1007\/978-3-031-16443-9_3"},{"key":"13_CR32","doi-asserted-by":"crossref","unstructured":"Xing, Z., Ye, T., Yang, Y., Liu, G., Zhu, L.: Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation. In: MICCAI. pp. 578\u2013588. Springer (2024)","DOI":"10.1007\/978-3-031-72111-3_54"},{"key":"13_CR33","unstructured":"Xu, Q., et\u00a0al.: Esp-medsam: efficient self-prompting sam for universal domain-generalized medical image segmentation. arXiv preprint arXiv:2407.14153 (2024)"},{"key":"13_CR34","unstructured":"Xu, Q., et al.: Hrmedseg: unlocking high-resolution medical image segmentation via memory-efficient attention modeling. arXiv preprint arXiv:2504.06205 (2025)"},{"key":"13_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106626","volume":"154","author":"Q Xu","year":"2023","unstructured":"Xu, Q., Ma, Z., Na, H., Duan, W.: 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":"13_CR36","doi-asserted-by":"crossref","unstructured":"Zhao, T., et\u00a0al.: A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities. Nature methods, pp. 1\u201311 (2024)","DOI":"10.1038\/s41592-024-02499-w"}],"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\/978-3-032-04927-8_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T17:49:52Z","timestamp":1787766592000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04927-8_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,21]]},"ISBN":["9783032049261","9783032049278"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04927-8_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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 declare no competing interests.","order":1,"name":"Ethics","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"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"}}]}}