{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:09:55Z","timestamp":1788998995798,"version":"build-2803163510"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049360","type":"print"},{"value":"9783032049377","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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\/978-3-032-04937-7_63","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:39:45Z","timestamp":1758260385000},"page":"663-673","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["UniSegDiff: Boosting Unified Lesion Segmentation via\u00a0a\u00a0Staged Diffusion Model"],"prefix":"10.1007","author":[{"given":"Yilong","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shijie","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihe","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weibing","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huchuan","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"63_CR1","unstructured":"Covid-19 CT lung and infection segmentation dataset (2020). https:\/\/medicalsegmentation.com\/covid19\/. Accessed 25 June 2025"},{"key":"63_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104863","volume":"28","author":"W Al-Dhabyani","year":"2020","unstructured":"Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data Brief 28, 104863 (2020)","journal-title":"Data Brief"},{"key":"63_CR3","unstructured":"Amit, T., Shaharbany, T., Nachmani, E., Wolf, L.: Segdiff: image segmentation with diffusion probabilistic models. arXiv preprint arXiv:2112.00390 (2021)"},{"key":"63_CR4","unstructured":"Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"issue":"10","key":"63_CR5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0140381","volume":"10","author":"J Cheng","year":"2015","unstructured":"Cheng, J., et al.: Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS ONE 10(10), e0140381 (2015)","journal-title":"PLoS ONE"},{"issue":"6","key":"63_CR6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0157112","volume":"11","author":"J Cheng","year":"2016","unstructured":"Cheng, J., et al.: Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation. PLoS ONE 11(6), e0157112 (2016)","journal-title":"PLoS ONE"},{"key":"63_CR7","doi-asserted-by":"crossref","unstructured":"Fan, D.P., et al.: Pranet: parallel reverse attention network for polyp segmentation. In: MICCAI, pp. 263\u2013273. Springer (2020)","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"63_CR8","doi-asserted-by":"crossref","unstructured":"Hejrati, B., Banerjee, S., Glide-Hurst, C., Dong, M.: Conditional diffusion model with spatial attention and latent embedding for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 202\u2013212. Springer (2024)","DOI":"10.1007\/978-3-031-72114-4_20"},{"key":"63_CR9","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"63_CR10","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1038\/s41597-024-03844-6","volume":"11","author":"Y Hu","year":"2024","unstructured":"Hu, Y., et al.: AMD-SD: an optical coherence tomography image dataset for wet AMD lesions segmentation. Sci. Data 11(1), 1014 (2024)","journal-title":"Sci. Data"},{"key":"63_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/978-3-030-37734-2_37","volume-title":"MultiMedia Modeling","author":"D Jha","year":"2020","unstructured":"Jha, D., et al.: Kvasir-SEG: a segmented polyp dataset. In: Ro, Y.M., et al. (eds.) MMM 2020. LNCS, vol. 11962, pp. 451\u2013462. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-37734-2_37"},{"key":"63_CR12","unstructured":"Jun, M., et al.: COVID-19 CT lung and infection segmentation dataset (2020)"},{"issue":"3","key":"63_CR13","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1148\/radiol.212182","volume":"304","author":"RY Kim","year":"2022","unstructured":"Kim, R.Y., et al.: Artificial intelligence tool for assessment of indeterminate pulmonary nodules detected with CT. Radiology 304(3), 683\u2013691 (2022)","journal-title":"Radiology"},{"key":"63_CR14","doi-asserted-by":"crossref","unstructured":"Lin, T., Chen, Z., Yan, Z., Yu, W., Zheng, F.: Stable diffusion segmentation for biomedical images with single-step reverse process. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 656\u2013666. Springer (2024)","DOI":"10.1007\/978-3-031-72111-3_62"},{"key":"63_CR15","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: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 3819\u20133827 (2024)","DOI":"10.1609\/aaai.v38i4.28173"},{"key":"63_CR16","unstructured":"Parmar, G., Park, T., Narasimhan, S., Zhu, J.Y.: One-step image translation with text-to-image models. arXiv preprint arXiv:2403.12036 (2024)"},{"key":"63_CR17","doi-asserted-by":"crossref","unstructured":"Rahman, A., Valanarasu, J.M.J., Hacihaliloglu, I., Patel, V.M.: Ambiguous medical image segmentation using diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11536\u201311546 (2023)","DOI":"10.1109\/CVPR52729.2023.01110"},{"key":"63_CR18","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":"63_CR19","doi-asserted-by":"publisher","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","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"63_CR20","doi-asserted-by":"crossref","unstructured":"Roy, S., et al.: Mednext: transformer-driven scaling of convnets for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 405\u2013415. Springer (2023)","DOI":"10.1007\/978-3-031-43901-8_39"},{"key":"63_CR21","doi-asserted-by":"crossref","unstructured":"Ruan, J., Xie, M., Gao, J., Liu, T., Fu, Y.: Ege-unet: an efficient group enhanced unet for skin lesion segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 481\u2013490. Springer (2023)","DOI":"10.1007\/978-3-031-43901-8_46"},{"key":"63_CR22","doi-asserted-by":"publisher","first-page":"1114673","DOI":"10.3389\/fmed.2023.1114673","volume":"10","author":"L Shi","year":"2023","unstructured":"Shi, L., et al.: EBHI-seg: a novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks. Front. Med. 10, 1114673 (2023)","journal-title":"Front. Med."},{"key":"63_CR23","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s11548-013-0926-3","volume":"9","author":"J Silva","year":"2014","unstructured":"Silva, J., Histace, A., Romain, O., Dray, X., Granado, B.: Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. Int. J. Comput. Assist. Radiol. Surg. 9, 283\u2013293 (2014)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"63_CR24","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)"},{"issue":"2","key":"63_CR25","first-page":"630","volume":"35","author":"N Tajbakhsh","year":"2015","unstructured":"Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automated polyp detection in colonoscopy videos using shape and context information. IEEE TMI 35(2), 630\u2013644 (2015)","journal-title":"IEEE TMI"},{"issue":"1","key":"63_CR26","first-page":"4037190","volume":"2017","author":"D V\u00e1zquez","year":"2017","unstructured":"V\u00e1zquez, D., et al.: A benchmark for endoluminal scene segmentation of colonoscopy images. J. Healthc. Eng. 2017(1), 4037190 (2017)","journal-title":"J. Healthc. Eng."},{"issue":"7","key":"63_CR27","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1109\/TMI.2004.828354","volume":"23","author":"SK Warfield","year":"2004","unstructured":"Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23(7), 903\u2013921 (2004)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"63_CR28","doi-asserted-by":"crossref","unstructured":"Wu, J., Ji, W., Fu, H., Xu, M., Jin, Y., Xu, Y.: Medsegdiff-v2: diffusion-based medical image segmentation with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 6030\u20136038 (2024)","DOI":"10.1609\/aaai.v38i6.28418"},{"key":"63_CR29","unstructured":"Zhao, X., et al.: Spider: a unified framework for context-dependent concept understanding. arXiv preprint arXiv:2405.01002 (2024)"},{"key":"63_CR30","doi-asserted-by":"crossref","unstructured":"Zhu, L., et al.: Make-a-volume: leveraging latent diffusion models for cross-modality 3D brain MRI synthesis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 592\u2013601. Springer (2023)","DOI":"10.1007\/978-3-031-43999-5_56"},{"key":"63_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-32226-7_1","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Z Zhu","year":"2019","unstructured":"Zhu, Z., Xia, Y., Xie, L., Fishman, E.K., Yuille, A.L.: Multi-scale coarse-to-fine segmentation for screening pancreatic ductal adenocarcinoma. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 3\u201312. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_1"}],"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\/978-3-032-04937-7_63","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T23:23:34Z","timestamp":1788996214000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04937-7_63"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049360","9783032049377"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04937-7_63","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","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","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"}}]}}