{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T14:06:59Z","timestamp":1742998019996,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031721137"},{"type":"electronic","value":"9783031721144"}],"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\/978-3-031-72114-4_63","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T13:01:43Z","timestamp":1727874103000},"page":"659-669","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unified Prompt-Visual Interactive Segmentation of Clinical Target Volume in CT for Nasopharyngeal Carcinoma with Prior Anatomical Information"],"prefix":"10.1007","author":[{"given":"Hee Guan","family":"Khor","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihua","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sijuan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaobin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bai","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longfei","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongen","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"63_CR1","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.jocs.2017.03.021","volume":"21","author":"MA Mohammed","year":"2017","unstructured":"Mohammed, M.A., Abd Ghani, M.K., Hamed, R.I., Ibrahim, D.A.: Review on nasopharyngeal carcinoma: concepts, methods of analysis, segmentation, classification, prediction and impact: a review of the research literature. J. Comput. Sci. 21, 283\u2013298 (2017)","journal-title":"J. Comput. Sci."},{"key":"63_CR2","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.radonc.2017.10.032","volume":"126","author":"AW Lee","year":"2018","unstructured":"Lee, A.W., et al.: International guideline for the delineation of the clinical target volumes (CTV) for nasopharyngeal carcinoma. Radiother. Oncol. 126, 25\u201336 (2018)","journal-title":"Radiother. Oncol."},{"key":"63_CR3","doi-asserted-by":"publisher","unstructured":"Tong, Y., et al.: The heterogeneous CTV-PTV margins should be given for different parts of Tumors during tomotherapy. Oncotarget. 8, 89086\u201389094 (2017). https:\/\/doi.org\/10.18632\/oncotarget.21631","DOI":"10.18632\/oncotarget.21631"},{"issue":"8","key":"63_CR4","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1007\/s00066-006-1463-6","volume":"182","author":"W Jeanneret-Sozzi","year":"2006","unstructured":"Jeanneret-Sozzi, W., Moeckli, R., Valley, J.-F., Zouhair, A., Ozsahin, E.M., Mirimanoff, R.-O., on Behalf SASRO* of SASRO*: The reasons for discrepancies in target volume delineation: a SASRO study on head-and-neck and prostate cancers. Strahlenther. Onkol. 182(8), 450\u2013457 (2006). https:\/\/doi.org\/10.1007\/s00066-006-1463-6","journal-title":"Strahlenther. Onkol."},{"key":"63_CR5","doi-asserted-by":"publisher","first-page":"S444","DOI":"10.1016\/j.ijrobp.2010.07.1044","volume":"78","author":"MU Feng","year":"2010","unstructured":"Feng, M.U., Demiroz, C., Vineberg, K.A., Balter, J.M., Eisbruch, A.: Intra-observer variability of organs at risk for head and neck cancer: geometric and dosimetric consequences. Int. J. Radiat. Oncol. Biol. Phys. 78, S444\u2013S445 (2010)","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"63_CR6","doi-asserted-by":"publisher","first-page":"E545","DOI":"10.1016\/j.ijrobp.2016.06.1993","volume":"96","author":"Y Kim","year":"2016","unstructured":"Kim, Y., et al.: Impact of contouring accuracy on expected tumor control probability for head and neck cancer: semiautomated segmentation versus manual contouring. Int. J. Radiat. Oncol. Biol. Phys. 96, E545 (2016)","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"63_CR7","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/978-3-031-43990-2_48","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8\u201312, 2023, Proceedings, Part VII","author":"Y Sun","year":"2023","unstructured":"Sun, Y., Khor, H.G., Huang, S., Chen, Qi., Wang, S., Yang, X., Liao, H.: Second-course esophageal gross tumor volume segmentation in\u00a0CT with\u00a0prior anatomical and\u00a0radiotherapy information. In: Greenspan, H., Madabhushi, A., Mousavi, P., Salcudean, S., Duncan, J., Syeda-Mahmood, T., Taylor, R. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8\u201312, 2023, Proceedings, Part VII, pp. 511\u2013520. Springer Nature Switzerland, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43990-2_48"},{"key":"63_CR8","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.3389\/fonc.2020.01134","volume":"10","author":"X Xue","year":"2020","unstructured":"Xue, X., et al.: Sequential and iterative auto-segmentation of high-risk clinical target volume for radiotherapy of nasopharyngeal carcinoma in planning CT images. Front. Oncol. 10, 1134 (2020)","journal-title":"Front. Oncol."},{"key":"63_CR9","doi-asserted-by":"publisher","first-page":"315","DOI":"10.3389\/fonc.2017.00315","volume":"7","author":"K Men","year":"2017","unstructured":"Men, K., et al.: Deep deconvolutional neural network for target segmentation of nasopharyngeal cancer in planning computed tomography images. Front. Oncol. 7, 315 (2017). https:\/\/doi.org\/10.3389\/fonc.2017.00315","journal-title":"Front. Oncol."},{"key":"63_CR10","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.ejmp.2018.05.006","volume":"50","author":"K Men","year":"2018","unstructured":"Men, K., et al.: Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning. Physica Med. 50, 13\u201319 (2018)","journal-title":"Physica Med."},{"key":"63_CR11","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.meddos.2022.09.004","volume":"48","author":"S Kihara","year":"2023","unstructured":"Kihara, S., et al.: Clinical target volume segmentation based on gross tumor volume using deep learning for head and neck cancer treatment. Med. Dosim. 48, 20\u201324 (2023)","journal-title":"Med. Dosim."},{"key":"63_CR12","doi-asserted-by":"publisher","first-page":"101909","DOI":"10.1016\/j.media.2020.101909","volume":"68","author":"D Jin","year":"2021","unstructured":"Jin, D., et al.: DeepTarget: gross tumor and clinical target volume segmentation in esophageal cancer radiotherapy. Med. Image Anal. 68, 101909 (2021)","journal-title":"Med. Image Anal."},{"key":"63_CR13","unstructured":"Kirillov, A., et al.: Segment anything. arXiv preprint arXiv:2304.02643. (2023)"},{"key":"63_CR14","unstructured":"Tang, L., Xiao, H., Li, B.: Can sam segment anything? when sam meets camouflaged object detection. arXiv preprint arXiv:2304.04709 (2023)"},{"key":"63_CR15","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":"63_CR16","unstructured":"Cheng, J., et al.: SAM-Med2D. arXiv preprint arXiv:2308.16184 (2023)"},{"key":"63_CR17","unstructured":"Wang, H., et al.: SAM-Med3D. arXiv preprint arXiv:2310.15161 (2023)"},{"key":"63_CR18","unstructured":"Hu, E.J., et al.: Lora: low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021)"},{"key":"63_CR19","unstructured":"Luo, X., et al.: SegRap2023: a benchmark of organs-at-risk and gross Tumor volume segmentation for radiotherapy planning of nasopharyngeal carcinoma. arXiv preprint arXiv:2312.09576 (2023)"},{"key":"63_CR20","doi-asserted-by":"publisher","first-page":"1917","DOI":"10.1002\/mp.16197","volume":"50","author":"G Podobnik","year":"2023","unstructured":"Podobnik, G., Strojan, P., Peterlin, P., Ibragimov, B., Vrtovec, T.: HaN-Seg: the head and neck organ-at-risk CT and MR segmentation dataset. Med. Phys. 50, 1917\u20131927 (2023)","journal-title":"Med. Phys."},{"issue":"1","key":"63_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-015-0068-x","volume":"15","author":"AA Taha","year":"2015","unstructured":"Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imag. 15(1), 1\u201328 (2015). https:\/\/doi.org\/10.1186\/s12880-015-0068-x","journal-title":"BMC Med. Imag."},{"key":"63_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"63_CR23","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"63_CR24","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"63_CR25","unstructured":"Cardoso, M.J., et al.: Monai: an open-source framework for deep learning in healthcare. arXiv preprint arXiv:2211.02701 (2022)"},{"key":"63_CR26","doi-asserted-by":"publisher","first-page":"315","DOI":"10.3389\/fonc.2017.00315","volume":"7","author":"K Men","year":"2017","unstructured":"Men, K., et al.: Deep deconvolutional neural network for target segmentation of nasopharyngeal cancer in planning computed tomography images. Front. Oncol. 7, 315 (2017)","journal-title":"Front. Oncol."},{"key":"63_CR27","doi-asserted-by":"crossref","unstructured":"Isensee, F., et al.: nnU-Net: self-adapting framework for U-Net-based medical image segmentation. arXiv preprint arXiv:1809.10486 (2018)","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"63_CR28","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., 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"}],"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\/978-3-031-72114-4_63","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T13:08:41Z","timestamp":1727874521000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72114-4_63"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721137","9783031721144"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72114-4_63","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":"3 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"}}]}}