{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T03:48:45Z","timestamp":1743133725801,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031734823"},{"type":"electronic","value":"9783031734830"}],"license":[{"start":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T00:00:00Z","timestamp":1727913600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T00:00:00Z","timestamp":1727913600000},"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\/978-3-031-73483-0_1","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:02:53Z","timestamp":1727870573000},"page":"3-13","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing the\u00a0Efficacy of\u00a0Foundation Models in\u00a0Pancreas Segmentation"],"prefix":"10.1007","author":[{"given":"Emanuele","family":"Rapisarda","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alessandro Giuseppe","family":"Gravagno","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Salvatore","family":"Calcagno","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniela","family":"Giordano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Busireddy, K.K., et al.: Pancreatitis-imaging approach. World J. Gastrointest. Pathophysiol. 5(3), 252\u2013270 (2014)","DOI":"10.4291\/wjgp.v5.i3.252"},{"issue":"1","key":"1_CR2","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s00125-015-3752-z","volume":"59","author":"ML Campbell-Thompson","year":"2016","unstructured":"Campbell-Thompson, M.L.: The influence of type 1 diabetes on pancreatic weight. Diabetologia 59(1), 217\u2013221 (2016)","journal-title":"Diabetologia"},{"key":"1_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"key":"1_CR4","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. http:\/\/arxiv.org\/abs\/2010.11929"},{"issue":"1","key":"1_CR5","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1109\/TIP.2016.2624198","volume":"26","author":"A Farag","year":"2017","unstructured":"Farag, A., Lu, L., Roth, H.R., Liu, J., Turkbey, E., Summers, R.M.: A bottom-up approach for pancreas segmentation using cascaded superpixels and (Deep) image patch labeling. IEEE Trans. Image Process. 26(1), 386\u2013399 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et al.: Segment Anything. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3992\u20134003 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"1_CR7","unstructured":"Lei, W., Wei, X., Zhang, X., Li, K., Zhang, S.: Medlsam: Localize and segment anything model for 3d medical images. arXiv preprint arXiv: (2023)"},{"issue":"1","key":"1_CR8","doi-asserted-by":"publisher","first-page":"4075","DOI":"10.1038\/s41598-022-07848-3","volume":"12","author":"SH Lim","year":"2022","unstructured":"Lim, S.H., Kim, Y.J., Park, Y.H., Kim, D., Kim, K.G., Lee, D.H.: Automated pancreas segmentation and volumetry using deep neural network on computed tomography. Sci. Rep. 12(1), 4075 (2022)","journal-title":"Sci. Rep."},{"key":"1_CR9","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma, J., He, Y., Li, F., et al.: Segment anything in medical images. Nat. Commun. 15, 654 (2024)","journal-title":"Nat. Commun."},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Nishio, M., Noguchi, S., Fujimoto, K.: Automatic pancreas segmentation using coarse-scaled 2D model of deep learning: usefulness of data augmentation and deep u-net. Appl. Sci. 10(10) (2020)","DOI":"10.3390\/app10103360"},{"key":"1_CR11","unstructured":"Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas. In: MIDL (2018)"},{"key":"1_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1007\/978-3-030-87589-3_25","volume-title":"Machine Learning in Medical Imaging","author":"F Proietto Salanitri","year":"2021","unstructured":"Proietto Salanitri, F., Bellitto, G., Irmakci, I., Palazzo, S., Bagci, U., Spampinato, C.: Hierarchical 3D feature learning for pancreas segmentation. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds.) MLMI 2021. LNCS, vol. 12966, pp. 238\u2013247. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87589-3_25"},{"issue":"5","key":"1_CR13","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1111\/jdi.12816","volume":"9","author":"H Sasamori","year":"2018","unstructured":"Sasamori, H., Fukui, T., Hayashi, T., Yamamoto, T., Ohara, M., Yamamoto, S., Kobayashi, T., Hirano, T.: Analysis of pancreatic volume in acute-onset, slowly-progressive and fulminant type 1 diabetes in a Japanese population. J. Diabetes Investig. 9(5), 1091\u20131099 (2018)","journal-title":"J. Diabetes Investig."},{"key":"1_CR14","unstructured":"Wang, H., et al.: Sam-med3D (2023)"},{"key":"1_CR15","unstructured":"Zhang, Z., et al.: Large-scale multi-center CT and MRI segmentation of pancreas with deep learning (2024)"},{"issue":"1","key":"1_CR16","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.yacr.2023.05.001","volume":"5","author":"Z Zhang","year":"2023","unstructured":"Zhang, Z., Yao, L., Keles, E., Velichko, Y., Bagci, U.: Deep learning algorithms for pancreas segmentation from radiology scans: a review. Adv. Clin. Radiol. 5(1), 31\u201352 (2023)","journal-title":"Adv. Clin. Radiol."},{"key":"1_CR17","doi-asserted-by":"publisher","unstructured":"Zhou, Y., et al.: Hyper-pairing network for multi-phase pancreatic ductal adenocarcinoma segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11765 LNCS, pp. 155\u2013163 (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_18","DOI":"10.1007\/978-3-030-32245-8_18"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Pancreatic Disease Detection and Diagnosis, and Personalized Incremental Learning in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73483-0_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:40:06Z","timestamp":1727872806000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73483-0_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,3]]},"ISBN":["9783031734823","9783031734830"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73483-0_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,3]]},"assertion":[{"value":"3 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIPAD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on AI in Pancreatic Disease Detection and Diagnosis","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":"11 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":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aipad2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aipad-miccai.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}