{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T17:28:41Z","timestamp":1743010121918,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030804312"},{"type":"electronic","value":"9783030804329"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-80432-9_17","type":"book-chapter","created":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T23:08:25Z","timestamp":1625526505000},"page":"210-223","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Slice-to-Volume Registration Enables Automated Pancreas MRI Quantification in UK Biobank"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5836-737X","authenticated-orcid":false,"given":"Alexandre Triay","family":"Bagur","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6173-9007","authenticated-orcid":false,"given":"Paul","family":"Aljabar","sequence":"additional","affiliation":[]},{"given":"Zobair","family":"Arya","sequence":"additional","affiliation":[]},{"given":"John","family":"McGonigle","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0430-0353","authenticated-orcid":false,"given":"Sir Michael","family":"Brady","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3034-8986","authenticated-orcid":false,"given":"Daniel","family":"Bulte","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,6]]},"reference":[{"key":"17_CR1","doi-asserted-by":"publisher","unstructured":"Mathur, A., et al.: Nonalcoholic fatty pancreas disease. Hpb9(4), 312\u2013318 (2007). https:\/\/doi.org\/10.1080\/13651820701504157","DOI":"10.1080\/13651820701504157"},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Tariq, H., Nayudu, S., Akella, S., Glandt, M., Chilimuri, S.: Non-alcoholic fatty pancreatic disease: a review of literature. Gastroenterol. Res. 9(6), 87\u201391 (2016). http:\/\/www.gastrores.org\/index.php\/Gastrores\/article\/view\/731","DOI":"10.14740\/gr731w"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Mojtahed, A., et al.: Reference range of liver corrected T1 values in a population at low risk for fatty liver disease\u2013a UK Biobank sub-study, with an appendix of interesting cases. Abdom. Radiol. 44(1), 72\u201384 (2019). http:\/\/link.springer.com\/10.1007\/s00261-018-1701-2","DOI":"10.1007\/s00261-018-1701-2"},{"key":"17_CR4","doi-asserted-by":"publisher","unstructured":"Reeder, S.B., Hu, H.H., Sirlin, C.B.: Proton density fat-fraction: a standardized MR-based biomarker of tissue fat concentration. J. Magn. Reson. Imaging 36(5), 1011\u20131014 (2012). https:\/\/doi.org\/10.1002\/jmri.23741","DOI":"10.1002\/jmri.23741"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Saisho, Y., et al.: Pancreas volumes in humans from birth to age one hundred taking into account sex, obesity, and presence of type-2 diabetes. Clin. Anat. 20(8), 933\u2013942 (2007). https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/ca.20543","DOI":"10.1002\/ca.20543"},{"issue":"8","key":"17_CR6","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.1007\/s00125-016-3984-6","volume":"59","author":"A Al-Mrabeh","year":"2016","unstructured":"Al-Mrabeh, A., Hollingsworth, K.G., Steven, S., Taylor, R.: Morphology of the pancreas in type 2 diabetes: effect of weight loss with or without normalisation of insulin secretory capacity. Diabetologia 59(8), 1753\u20131759 (2016). https:\/\/doi.org\/10.1007\/s00125-016-3984-6","journal-title":"Diabetologia"},{"key":"17_CR7","doi-asserted-by":"publisher","unstructured":"Tirkes, T., Lin, C., Fogel, E.L., Sherman, S.S., Wang, Q., Sandrasegaran, K.: T 1 mapping for diagnosis of mild chronic pancreatitis. J. Magn. Reson. Imaging 45(4), 1171\u20131176 (2017). https:\/\/doi.org\/10.1002\/jmri.25428","DOI":"10.1002\/jmri.25428"},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"K\u00fchn, J.P., et al.: Pancreatic steatosis demonstrated at mr imaging in the general population: clinical relevance. Radiology 276(1), 129\u2013136 (2015). http:\/\/pubs.rsna.org\/doi\/10.1148\/radiol.15140446","DOI":"10.1148\/radiol.15140446"},{"key":"17_CR9","doi-asserted-by":"publisher","unstructured":"Littlejohns, T.J., et al.: The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat. Commun. 11(1), 2624 (2020). https:\/\/doi.org\/10.1038\/s41467-020-15948-9, www.nature.com\/articles\/s41467-020-15948-9","DOI":"10.1038\/s41467-020-15948-9"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Wilman, H.R., et al.: Characterisation of liver fat in the UK Biobank cohort. PLoS ONE 12(2), 1\u201314 (2017). http:\/\/dx.doi.org\/10.1371\/journal.pone.0172921","DOI":"10.1371\/journal.pone.0172921"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Hutton, C., Gyngell, M.L., Milanesi, M., Bagur, A., Brady, M.: Validation of a standardized MRI method for liver fat and T2* quantification. PLOS ONE 13(9), e0204175 (2018). https:\/\/dx.plos.org\/10.1371\/journal.pone.0204175","DOI":"10.1371\/journal.pone.0204175"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Tarroni, G., et al.: Large-scale quality control of cardiac imaging in population studies: application to UK Biobank. Sci. Rep. 10(1), 2408 (2020). http:\/\/www.nature.com\/articles\/s41598-020-58212-2","DOI":"10.1038\/s41598-020-58212-2"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Basty, N., Liu, Y., Cule, M., Thomas, E.L., Bell, J.D., Whitcher, B.: Automated measurement of pancreatic fat and iron concentration using multi-echo and T1-Weighted MRI data. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), vol. 2020-April, pp. 345\u2013348. IEEE (2020). https:\/\/ieeexplore.ieee.org\/document\/9098650\/","DOI":"10.1109\/ISBI45749.2020.9098650"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Genetic architecture of 11 abdominal organ traits derived from abdominal MRI using deep learning, pp. 1\u201366 (2020)","DOI":"10.1101\/2020.07.14.187070"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Ferrante, E., Paragios, N.: Slice-to-volume medical image registration: a survey. Med. Image Anal. 39, 101\u2013123 (2017). https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841517300701","DOI":"10.1016\/j.media.2017.04.010"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Hou, B., et al.: Predicting slice-to-volume transformation in presence of arbitrary subject motion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10434. LNCS, pp. 296\u2013304 (2017)","DOI":"10.1007\/978-3-319-66185-8_34"},{"key":"17_CR17","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/978-3-030-52791-4_11","volume-title":"Medical Image Understanding and Analysis","author":"AT Bagur","year":"2020","unstructured":"Bagur, A.T., Ridgway, G., McGonigle, J., Brady, S.M., Bulte, D.: Pancreas segmentation-derived biomarkers: volume and shape metrics in the UK Biobank imaging study. In: Papie\u017c, B.W., Namburete, A.I.L., Yaqub, M., Noble, J.A. (eds.) MIUA 2020. CCIS, vol. 1248, pp. 131\u2013142. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-52791-4_11"},{"key":"17_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/978-3-642-40811-3_24","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"MP Heinrich","year":"2013","unstructured":"Heinrich, M.P., Jenkinson, M., Papie\u017c, B.W., Brady, S.M., Schnabel, J.A.: Towards realtime multimodal fusion for image-guided interventions using self-similarities. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 187\u2013194. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40811-3_24"},{"key":"17_CR19","doi-asserted-by":"publisher","unstructured":"Nadarajah, C., et al.: Association of pancreatic fat content with type II diabetes mellitus. Clin. Radiol. 75(1), 51\u201356 (2020). https:\/\/doi.org\/10.1016\/j.crad.2019.05.027","DOI":"10.1016\/j.crad.2019.05.027"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Sakai, N.S., Taylor, S.A., Chouhan, M.D.: Obesity, metabolic disease and the pancreas-Quantitative imaging of pancreatic fat. Br. J. Radiol. 91(1089), 20180267 (2018). https:\/\/www.birpublications.org\/doi\/10.1259\/bjr.20180267","DOI":"10.1259\/bjr.20180267"},{"key":"17_CR21","unstructured":"Bagur, A.T., Ridgway, G., Brady, M., Bulte, D.: (Abstract accepted for presentation) Automated pancreas parts segmentation by groupwise registration and minimal annotation enables regional assessment of disease. In: ISMRM Annual Meeting (2021)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Understanding and Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-80432-9_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T17:31:51Z","timestamp":1710264711000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-80432-9_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030804312","9783030804329"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-80432-9_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"6 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIUA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Conference on Medical Image Understanding and Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Oxford","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miua2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miua2021.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"77","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,8","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}