{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:02:06Z","timestamp":1790208126125,"version":"4.1.0"},"reference-count":44,"publisher":"SPIE-Intl Soc Optical Eng","issue":"02","license":[{"start":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:00:00Z","timestamp":1744848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Vanderbilt University Medical Center Institutional Funding","id":[{"id":"https:\/\/ror.org\/05dq2gs74","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Institutes of Health (NIH) Common Fund","award":["U54DK134302"],"award-info":[{"award-number":["U54DK134302"]}]},{"name":"National Science Foundation Faculty Early Career Development","award":["1452485"],"award-info":[{"award-number":["1452485"]}],"id":[{"id":"https:\/\/ror.org\/02xjhr934","id-type":"ROR","asserted-by":"crossref"}]},{"name":"NIH","award":["2R01EB006136"],"award-info":[{"award-number":["2R01EB006136"]}]},{"name":"The Vanderbilt Institute for Clinical and Translational Research, Clinical and Translational Science Awards","award":["ULTR000445"],"award-info":[{"award-number":["ULTR000445"]}]},{"name":"Patient-Centered Outcome Research Institute","award":["CDRN-1306-04869"],"award-info":[{"award-number":["CDRN-1306-04869"]}],"id":[{"id":"https:\/\/ror.org\/014q65q44","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Integrated Training in Engineering and Diabetes","award":["T32 DK101003"],"award-info":[{"award-number":["T32 DK101003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Med. Imag."],"published-print":{"date-parts":[[2025,4,17]]},"abstract":"<jats:p>PurposeDiverse population demographics can lead to substantial variation in the human anatomy. Therefore, standard anatomical atlases are needed for interpreting organ-specific analyses. Among abdominal organs, the pancreas exhibits notable variability in volumetric morphology, shape, and appearance, complicating the generalization of population-wide features. Understanding the common features of a healthy pancreas is crucial for identifying biomarkers and diagnosing pancreatic diseases.ApproachWe propose a high-resolution CT atlas framework optimized for the healthy pancreas. We introduce a deep-learning-based preprocessing technique to extract abdominal ROIs and leverage a hierarchical registration pipeline to align pancreatic anatomy across populations. Briefly, DEEDS affine and non-rigid registration techniques are employed to transfer patient abdominal volumes to a fixed high-resolution atlas template. To generate and evaluate the pancreas atlas, multi-phase contrast CT scans of 443 subjects (aged 15 to 50 years, with no reported history of pancreatic disease) were processed.ResultsThe two-stage DEEDS affine and non-rigid registration outperforms other state-of-the-art tools, achieving the highest scores for pancreas label transfer across all phases (non-contrast: 0.497, arterial: 0.505, portal venous: 0.494, delayed: 0.497). External evaluation with 100 portal venous scans and 13 labeled abdominal organs shows a mean Dice score of 0.504. The low variance between the pancreases of registered subjects and the obtained pancreas atlas further illustrates the generalizability of the proposed method.ConclusionWe introduce a high-resolution pancreas atlas framework to generalize healthy biomarkers across populations with multi-contrast abdominal CT. The atlases and the associated pancreas organ labels are publicly available through the Human Biomolecular Atlas Program (HuBMAP).<\/jats:p>","DOI":"10.1117\/1.jmi.12.2.024006","type":"journal-article","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T20:27:22Z","timestamp":1744921642000},"page":"1-17","source":"Crossref","is-referenced-by-count":1,"title":["Multi-contrast computed tomography atlas of healthy pancreas with dense displacement sampling registration"],"prefix":"10.1117","volume":"12","author":[{"given":"Yinchi","family":"Zhou","sequence":"first","affiliation":[{"name":"Vanderbilt University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ho Hin","family":"Lee","sequence":"additional","affiliation":[{"name":"Vanderbilt University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yucheng","family":"Tang","sequence":"additional","affiliation":[{"name":"NVIDIA Corporation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Yu","sequence":"additional","affiliation":[{"name":"Vanderbilt University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Yang","sequence":"additional","affiliation":[{"name":"Vanderbilt University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3562-2688","authenticated-orcid":false,"given":"Michael E.","family":"Kim","sequence":"additional","affiliation":[{"name":"Vanderbilt University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucas W.","family":"Remedios","sequence":"additional","affiliation":[{"name":"Vanderbilt University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6376-4292","authenticated-orcid":false,"given":"Shunxing","family":"Bao","sequence":"additional","affiliation":[{"name":"Vanderbilt University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffery M.","family":"Spraggins","sequence":"additional","affiliation":[{"name":"Vanderbilt University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2096-8065","authenticated-orcid":false,"given":"Yuankai","family":"Huo","sequence":"additional","affiliation":[{"name":"Vanderbilt University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5733-2127","authenticated-orcid":false,"given":"Bennett A.","family":"Landman","sequence":"additional","affiliation":[{"name":"Vanderbilt University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"189","reference":[{"key":"r1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1629-x"},{"key":"r2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra072149"},{"key":"r3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2009.07.009"},{"key":"r4","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-019-01991-5"},{"key":"r5","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14706"},{"key":"r6","doi-asserted-by":"publisher","DOI":"10.1016\/S1052-5157(18)30458-6"},{"key":"r7","doi-asserted-by":"publisher","DOI":"10.3748\/wjg.v22.i44.9694"},{"key":"r8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2020.04.007"},{"key":"r9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2005.03.002"},{"key":"r10","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/bhh165"},{"key":"r11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0018746"},{"key":"r12","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.10.019"},{"key":"r13","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-00525-w"},{"key":"r14","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2587628"},{"key":"r15","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-020-0379-9"},{"key":"r16","doi-asserted-by":"publisher","DOI":"10.1117\/12.2580561"},{"key":"r17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105555"},{"key":"r18","doi-asserted-by":"publisher","DOI":"10.1117\/12.2608290"},{"key":"r19","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2007.07.007"},{"key":"r20","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2007.06.004"},{"key":"r21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00964"},{"key":"r22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-47118-1_8"},{"key":"r23","doi-asserted-by":"publisher","DOI":"10.1109\/42.796284"},{"key":"r24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2008.10.040"},{"key":"r25","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2574816"},{"key":"r26","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2897538"},{"key":"r27","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.07.006"},{"key":"r28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01070"},{"key":"r29","article-title":"A comparative analysis of registration tools: traditional vs deep learning approach on high-resolution tissue cleared data","author":"Nazib","year":"2018"},{"key":"r30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40811-3_24"},{"key":"r31","first-page":"27","article-title":"Multi-modal multi-atlas segmentation using discrete optimisation and self-similarities","author":"Heinrich","year":"2015"},{"key":"r32","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3058281"},{"key":"r33","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363745"},{"key":"r34","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101894"},{"key":"r35","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI48211.2021.9433975"},{"key":"r36","article-title":"3D UX-Net: a large kernel volumetric ConvNet modernizing hierarchical transformer for medical image segmentation","author":"Lee","year":"2023"},{"key":"r37","first-page":"574","article-title":"UNETR: transformers for 3D medical image segmentation","author":"Hatamizadeh","year":"2022"},{"key":"r38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"r39","first-page":"102939","article-title":"UNEST: local spatial representation learning with hierarchical transformer for efficient medical segmentation","author":"Yu","year":"2022"},{"key":"r40","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2013.2246577"},{"key":"r41","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.09.015"},{"key":"r42","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2009.09.002"},{"key":"r43","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.10091819"},{"key":"r44","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.181.4.1810987"}],"container-title":["Journal of Medical Imaging"],"original-title":[],"link":[{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-medical-imaging\/volume-12\/issue-02\/024006\/Multi-contrast-computed-tomography-atlas-of-healthy-pancreas-with-dense\/10.1117\/1.JMI.12.2.024006.full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T23:17:29Z","timestamp":1790205449000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-medical-imaging\/volume-12\/issue-02\/024006\/Multi-contrast-computed-tomography-atlas-of-healthy-pancreas-with-dense\/10.1117\/1.JMI.12.2.024006.full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,17]]},"references-count":44,"journal-issue":{"issue":"02","published-online":{"date-parts":[[2025,3,1]]}},"URL":"https:\/\/doi.org\/10.1117\/1.jmi.12.2.024006","relation":{},"ISSN":["2329-4302","2329-4310"],"issn-type":[{"value":"2329-4302","type":"print"},{"value":"2329-4310","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,17]]}}}