{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:59:47Z","timestamp":1775066387959,"version":"3.50.1"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031733826","type":"print"},{"value":"9783031733833","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73383-3_17","type":"book-chapter","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T12:02:00Z","timestamp":1730548920000},"page":"288-304","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Unleashing the\u00a0Power of\u00a0Prompt-Driven Nucleus Instance Segmentation"],"prefix":"10.1007","author":[{"given":"Zhongyi","family":"Shui","sequence":"first","affiliation":[]},{"given":"Yunlong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Chenglu","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Sunyi","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Jingxiong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Honglin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yuxuan","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Ruizhe","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Lin","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,3]]},"reference":[{"key":"17_CR1","unstructured":"Alberts, B., et al.: Essential cell biology. Garland Science (2015)"},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Chen, H., Qi, X., Yu, L., Heng, P.A.: Dcan: deep contour-aware networks for accurate gland segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2487\u20132496 (2016)","DOI":"10.1109\/CVPR.2016.273"},{"key":"17_CR3","unstructured":"Chen, J., Huang, Q., Chen, Y., Qian, L., Yu, C.: Enhancing nucleus segmentation with haru-net: a hybrid attention based residual u-blocks network. arXiv preprint arXiv:2308.03382 (2023)"},{"key":"17_CR4","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":"17_CR5","unstructured":"Cheng, J., Ye, J., Deng, Z., Chen, J., Li, T., Wang, H., Su, Y., Huang, Z., Chen, J., Jiang, L., et\u00a0al.: Sam-med2d. arXiv preprint arXiv:2308.16184 (2023)"},{"key":"17_CR6","unstructured":"Deng, R., et\u00a0al.: Segment anything model (sam) for digital pathology: assess zero-shot segmentation on whole slide imaging. arXiv preprint arXiv:2304.04155 (2023)"},{"issue":"19","key":"17_CR7","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac8594","volume":"67","author":"G Deshmukh","year":"2022","unstructured":"Deshmukh, G., Susladkar, O., Makwana, D., Mittal, S., et al.: Feednet: a feature enhanced encoder-decoder lstm network for nuclei instance segmentation for histopathological diagnosis. Phys. Med. Biol. 67(19), 195011 (2022)","journal-title":"Phys. Med. Biol."},{"key":"17_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/978-3-030-23937-4_2","volume-title":"Digital Pathology","author":"J Gamper","year":"2019","unstructured":"Gamper, J., Alemi Koohbanani, N., Benet, K., Khuram, A., Rajpoot, N.: PanNuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification. In: Reyes-Aldasoro, C.C., Janowczyk, A., Veta, M., Bankhead, P., Sirinukunwattana, K. (eds.) ECDP 2019. LNCS, vol. 11435, pp. 11\u201319. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-23937-4_2"},{"key":"17_CR9","unstructured":"Gamper, J., et al.: Pannuke dataset extension, insights and baselines. arXiv preprint arXiv:2003.10778 (2020)"},{"key":"17_CR10","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":"17_CR11","doi-asserted-by":"crossref","unstructured":"He, H., et al.: Cdnet: centripetal direction network for nuclear instance segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4026\u20134035 (2021)","DOI":"10.1109\/ICCV48922.2021.00399"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"He, H., et al.: Toposeg: topology-aware nuclear instance segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 21307\u201321316 (2023)","DOI":"10.1109\/ICCV51070.2023.01948"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"H\u00f6rst, F., et\u00a0al.: Cellvit: vision transformers for precise cell segmentation and classification. arXiv preprint arXiv:2306.15350 (2023)","DOI":"10.1016\/j.media.2024.103143"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Huang, Y., et\u00a0al.: Segment anything model for medical images? Medical Image Analysis p. 103061 (2023)","DOI":"10.1016\/j.media.2023.103061"},{"key":"17_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2022.02.020","volume":"151","author":"T Ilyas","year":"2022","unstructured":"Ilyas, T., Mannan, Z.I., Khan, A., Azam, S., Kim, H., De Boer, F.: Tsfd-net: tissue specific feature distillation network for nuclei segmentation and classification. Neural Netw. 151, 1\u201315 (2022)","journal-title":"Neural Netw."},{"key":"17_CR17","unstructured":"Kirillov, A., et\u00a0al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)"},{"issue":"7","key":"17_CR18","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":"17_CR19","unstructured":"Lei, W., Wei, X., Zhang, X., Li, K., Zhang, S.: Medlsam: localize and segment anything model for 3d medical images. arXiv preprint arXiv:2306.14752 (2023)"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"17_CR22","unstructured":"Lin, X., Xiang, Y., Zhang, L., Yang, X., Yan, Z., Yu, L.: Samus: adapting segment anything model for clinically-friendly and generalizable ultrasound image segmentation. arXiv preprint arXiv:2309.06824 (2023)"},{"key":"17_CR23","unstructured":"Lou, W., et al.: Structure embedded nucleus classification for histopathology images. arXiv preprint arXiv:2302.11416 (2023)"},{"key":"17_CR24","doi-asserted-by":"crossref","unstructured":"Ma, J., Wang, B.: Segment anything in medical images. arXiv preprint arXiv:2304.12306 (2023)","DOI":"10.1038\/s41467-024-44824-z"},{"key":"17_CR25","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":"17_CR26","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)"},{"issue":"2","key":"17_CR27","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1109\/TMI.2018.2865709","volume":"38","author":"P Naylor","year":"2018","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 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"17_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1007\/978-3-030-32239-7_42","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"H Qu","year":"2019","unstructured":"Qu, H., Yan, Z., Riedlinger, G.M., De, S., Metaxas, D.N.: Improving nuclei\/gland instance segmentation in histopathology images by full resolution neural network and spatial constrained loss. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 378\u2013386. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_42"},{"key":"17_CR29","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.media.2018.12.003","volume":"52","author":"SEA Raza","year":"2019","unstructured":"Raza, S.E.A., Cheung, L., Shaban, M., Graham, S., Epstein, D., Pelengaris, S., Khan, M., Rajpoot, N.M.: Micro-net: a unified model for segmentation of various objects in microscopy images. Med. Image Anal. 52, 160\u2013173 (2019)","journal-title":"Med. Image Anal."},{"key":"17_CR30","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":"17_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/978-3-030-00934-2_30","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"U Schmidt","year":"2018","unstructured":"Schmidt, U., Weigert, M., Broaddus, C., Myers, G.: Cell detection with star-convex polygons. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 265\u2013273. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_30"},{"key":"17_CR32","doi-asserted-by":"crossref","unstructured":"Song, Q., et al.: Rethinking counting and localization in crowds: a purely point-based framework. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3365\u20133374 (2021)","DOI":"10.1109\/ICCV48922.2021.00335"},{"key":"17_CR33","doi-asserted-by":"crossref","unstructured":"Vu, Q.D., et\u00a0al.: Methods for segmentation and classification of digital microscopy tissue images. Frontiers in bioengineering and biotechnology p.\u00a053 (2019)","DOI":"10.3389\/fbioe.2019.00053"},{"key":"17_CR34","unstructured":"Wang, H., et\u00a0al.: Sam-med3d. arXiv preprint arXiv:2310.15161 (2023)"},{"key":"17_CR35","unstructured":"Wu, J., et al.: Medical sam adapter: adapting segment anything model for medical image segmentation. arXiv preprint arXiv:2304.12620 (2023)"},{"key":"17_CR36","doi-asserted-by":"publisher","unstructured":"Xu, Q., Kuang, W., Zhang, Z., Bao, X., Chen, H., Duan, W.: Sppnet: a single-point prompt network for nuclei image segmentation. In: International Workshop on Machine Learning in Medical Imaging, pp. 227\u2013236. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-45673-2_23","DOI":"10.1007\/978-3-031-45673-2_23"},{"key":"17_CR37","doi-asserted-by":"crossref","unstructured":"Yao, K., Huang, K., Sun, J., Hussain, A.: Pointnu-net: Keypoint-assisted convolutional neural network for simultaneous multi-tissue histology nuclei segmentation and classification. IEEE Trans. Emerging Topics Comput. Intell. (2023)","DOI":"10.1109\/TETCI.2023.3281864"},{"key":"17_CR38","unstructured":"Zhang, C., et al.:Faster segment anything: towards lightweight sam for mobile applications. arXiv preprint arXiv:2306.14289 (2023)"},{"key":"17_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, K., Liu, D.: Customized segment anything model for medical image segmentation. arXiv preprint arXiv:2304.13785 (2023)","DOI":"10.2139\/ssrn.4495221"},{"key":"17_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101786","volume":"65","author":"B Zhao","year":"2020","unstructured":"Zhao, B., Chen, X., Li, Z., Yu, Z., Yao, S., Yan, L., Wang, Y., Liu, Z., Liang, C., Han, C.: Triple u-net: hematoxylin-aware nuclei segmentation with progressive dense feature aggregation. Med. Image Anal. 65, 101786 (2020)","journal-title":"Med. Image Anal."},{"key":"17_CR41","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Onder, O.F., Dou, Q., Tsougenis, E., Chen, H., Heng, P.A.: Cia-net: robust nuclei instance segmentation with contour-aware information aggregation. In: Information Processing in Medical Imaging: 26th International Conference, IPMI 2019, Hong Kong, China, June 2\u20137, 2019, Proceedings 26, pp. 682\u2013693. Springer (2019)","DOI":"10.1007\/978-3-030-20351-1_53"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73383-3_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T12:09:51Z","timestamp":1730549391000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73383-3_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,3]]},"ISBN":["9783031733826","9783031733833"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73383-3_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,3]]},"assertion":[{"value":"3 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}