{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T12:44:47Z","timestamp":1776689087821,"version":"3.51.2"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032184795","type":"print"},{"value":"9783032184801","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-3-032-18480-1_35","type":"book-chapter","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T11:53:43Z","timestamp":1776686023000},"page":"341-349","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid Framework for Automated Pancreas Segmentation in Abdominal CT Imaging"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9366-8486","authenticated-orcid":false,"given":"Rupam","family":"Sah","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suchi","family":"Jain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renu","family":"Dhir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,21]]},"reference":[{"issue":"8","key":"35_CR1","doi-asserted-by":"publisher","first-page":"1971","DOI":"10.1109\/TMI.2019.2911588","volume":"38","author":"Y Man","year":"2019","unstructured":"Man, Y., Huang, Y., Feng, J., Li, X., Wu, F.: Deep Q learning driven CT pancreas segmentation with geometry-aware U-Net. IEEE Trans. Med. Imaging 38(8), 1971\u20131980 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"35_CR2","doi-asserted-by":"publisher","first-page":"2906","DOI":"10.1109\/ACCESS.2019.2961125","volume":"8","author":"S Liu","year":"2019","unstructured":"Liu, S., et al.: Automatic pancreas segmentation via coarse location and ensemble learning. IEEE Access 8, 2906\u20132914 (2019)","journal-title":"IEEE Access"},{"key":"35_CR3","doi-asserted-by":"publisher","first-page":"172871","DOI":"10.1109\/ACCESS.2019.2956550","volume":"7","author":"L Lu","year":"2019","unstructured":"Lu, L., Jian, L., Luo, J., Xiao, B.: Pancreatic segmentation via ringed residual U-Net. IEEE Access 7, 172871\u2013172878 (2019)","journal-title":"IEEE Access"},{"issue":"1","key":"35_CR4","first-page":"3284493","volume":"2021","author":"M Huang","year":"2021","unstructured":"Huang, M., Huang, C., Yuan, J., Kong, D.: A semiautomated deep learning approach for pancreas segmentation. J. Healthc. Eng. 2021(1), 3284493 (2021)","journal-title":"J. Healthc. Eng."},{"key":"35_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-021-00729-7","volume":"22","author":"R Roger","year":"2022","unstructured":"Roger, R., et al.: Deep learning-based pancreas volume assessment in individuals with type 1 diabetes. BMC Med. Imaging 22, 1\u20135 (2022)","journal-title":"BMC Med. Imaging"},{"key":"35_CR6","doi-asserted-by":"crossref","unstructured":"Saraswathi, H.S., Rafi, M.: U-net-based pancreas tumor segmentation from abdominal CT images. Int. J. Adv. Comput. Sci. Appl. 14(7) (2023)","DOI":"10.14569\/IJACSA.2023.0140770"},{"key":"35_CR7","doi-asserted-by":"crossref","unstructured":"Devendhar, T.: U-net based pancreas segmentation from computed tomography images. In: 2024 Third International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN), pp. 1\u20135. IEEE (2024)","DOI":"10.1109\/ICSTSN61422.2024.10671042"},{"issue":"1","key":"35_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":"35_CR9","doi-asserted-by":"crossref","unstructured":"Sah, R., Dhir, R., Jain, S.: AI-driven approaches for improved detection and diagnosis of pancreatic cancer. In 2025 International Conference on Ambient Intelligence in Health Care (ICAIHC), pp. 1\u20136. IEEE (2025)","DOI":"10.1109\/ICAIHC64101.2025.10956704"},{"key":"35_CR10","unstructured":"O\u2019shea, K., Nash, R.:. An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458 (2015)"},{"key":"35_CR11","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"1","key":"35_CR12","first-page":"4189781","volume":"2022","author":"XX Yin","year":"2022","unstructured":"Yin, X.X., Sun, L., Fu, Y., Lu, R., Zhang, Y.: [Retracted] U-net-based medical image segmentation. J. Healthc. Eng. 2022(1), 4189781 (2022)","journal-title":"J. Healthc. Eng."},{"issue":"4","key":"35_CR13","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1007\/s00530-023-01115-9","volume":"29","author":"S Jain","year":"2023","unstructured":"Jain, S., Sikka, G., Dhir, R.: An automatic cascaded approach for pancreas segmentation via an unsupervised localization using 3D CT volumes. Multimedia Syst. 29(4), 2337\u20132349 (2023)","journal-title":"Multimedia Syst."},{"key":"35_CR14","doi-asserted-by":"crossref","unstructured":"Jain, S., Gupta, S., Gulati, A.: An adaptive hybrid technique for pancreas segmentation using CT image sequences. In: 2015 International Conference on Signal Processing, Computing and Control (ISPCC), pp. 272\u2013276. IEEE (2015)","DOI":"10.1109\/ISPCC.2015.7375039"},{"key":"35_CR15","unstructured":"Cao, H., et al.: Swin-Unet: Unet-like pure transformer for medical image segmentation. arXiv preprint arXiv:2105.05537 (2021)"},{"key":"35_CR16","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume":"9351","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional net- works for biomedical image segmentation. Lect. Notes Comput. Sci. 9351, 234\u2013241 (2015)","journal-title":"Lect. Notes Comput. Sci."},{"key":"35_CR17","unstructured":"Oktay, O., et al.: Attention U-Net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-18480-1_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T11:53:48Z","timestamp":1776686028000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-18480-1_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032184795","9783032184801"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-18480-1_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"21 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no conflict of interest.\n                      Information on Data Availability.\n                      The data supporting the results of this study can be accessed from the NIH pancreas-CT Dataset at\n                      \n                      .","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest."}},{"value":"PReMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition and Machine Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Delhi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"11 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"premi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/premi25-git-dev-ashirbad97s-projects.vercel.app\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}