{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T04:58:30Z","timestamp":1785819510060,"version":"3.56.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T00:00:00Z","timestamp":1646697600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T00:00:00Z","timestamp":1646697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Science Foundation","award":["1500124"],"award-info":[{"award-number":["1500124"]}]},{"name":"National Institute of Health","award":["LM012946-01"],"award-info":[{"award-number":["LM012946-01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Both early detection and severity assessment of liver trauma are critical for optimal triage and management of trauma patients. Current trauma protocols utilize computed tomography (CT) assessment of injuries in a subjective and qualitative (v.s. quantitative) fashion, shortcomings which could both be addressed by automated computer-aided systems that are capable of generating real-time reproducible and quantitative information. This study outlines an end-to-end pipeline to calculate the percentage of the liver parenchyma disrupted by trauma, an important component of the American Association for the Surgery of Trauma (AAST) liver injury scale, the primary tool to assess liver trauma severity at CT.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This framework comprises deep convolutional neural networks that first generate initial masks of both liver parenchyma (including normal and affected liver) and regions affected by trauma using three dimensional contrast-enhanced CT scans. Next, during the post-processing step, human domain knowledge about the location and intensity distribution of liver trauma is integrated into the model to avoid false positive regions. After generating the liver parenchyma and trauma masks, the corresponding volumes are calculated. Liver parenchymal disruption is then computed as the volume of the liver parenchyma that is disrupted by trauma.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed model was trained and validated on an internal dataset from the University of Michigan Health System (UMHS) including 77 CT scans (34 with and 43 without liver parenchymal trauma). The Dice\/recall\/precision coefficients of the proposed segmentation models are 96.13\/96.00\/96.35% and 51.21\/53.20\/56.76%, respectively, in segmenting liver parenchyma and liver trauma regions. In volume-based severity analysis, the proposed model yields a linear regression relation of 0.95 in estimating the percentage of liver parenchyma disrupted by trauma. The model shows an accurate performance in avoiding false positives for patients without any liver parenchymal trauma. These results indicate that the model is generalizable on patients with pre-existing liver conditions, including fatty livers and congestive hepatopathy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The proposed algorithms are able to accurately segment the liver and the regions affected by trauma. This pipeline demonstrates an accurate performance in estimating the percentage of liver parenchyma that is affected by trauma. Such a system can aid critical care medical personnel by providing a reproducible quantitative assessment of liver trauma as an alternative to the sometimes subjective AAST grading system that is used currently.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12880-022-00759-9","type":"journal-article","created":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T18:02:41Z","timestamp":1646762561000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["A deep learning framework for automated detection and quantitative assessment of liver trauma"],"prefix":"10.1186","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1200-5274","authenticated-orcid":false,"given":"Negar","family":"Farzaneh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erica B.","family":"Stein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reza","family":"Soroushmehr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Gryak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kayvan","family":"Najarian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,8]]},"reference":[{"issue":"1","key":"759_CR1","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1097\/SLA.0000000000000600","volume":"260","author":"P Rhee","year":"2014","unstructured":"Rhee P, Joseph B, Pandit V, Aziz H, Vercruysse G, Kulvatunyou N, Friese RS. Increasing trauma deaths in the United States. Ann Surg. 2014;260(1):13\u201321.","journal-title":"Ann Surg"},{"key":"759_CR2","unstructured":"Taghavi S, Askari R. Liver trauma. In: StatPearls [Internet]. StatPearls Publishing. 2019."},{"issue":"1","key":"759_CR3","doi-asserted-by":"publisher","first-page":"114","DOI":"10.4103\/0974-2700.76846","volume":"4","author":"N Ahmed","year":"2011","unstructured":"Ahmed N, Vernick JJ. Management of liver trauma in adults. J Emerg Trauma Shock. 2011;4(1):114.","journal-title":"J Emerg Trauma Shock"},{"issue":"12","key":"759_CR4","doi-asserted-by":"publisher","first-page":"2522","DOI":"10.1007\/s00268-009-0215-z","volume":"33","author":"SA Badger","year":"2009","unstructured":"Badger SA, Barclay R, Campbell P, Mole DJ, Diamond T. Management of liver trauma. World J Surg. 2009;33(12):2522\u201337.","journal-title":"World J Surg"},{"issue":"4","key":"759_CR5","doi-asserted-by":"publisher","first-page":"193","DOI":"10.4103\/0974-2700.166590","volume":"8","author":"S Arumugam","year":"2015","unstructured":"Arumugam S, Al-Hassani A, El-Menyar A, Abdelrahman H, Parchani A, Peralta R, Zarour A, Al-Thani H. Frequency, causes and pattern of abdominal trauma: a 4-year descriptive analysis. J Emerg Trauma Shock. 2015;8(4):193.","journal-title":"J Emerg Trauma Shock"},{"issue":"4","key":"759_CR6","first-page":"775","volume":"90","author":"GL Piper","year":"2010","unstructured":"Piper GL, Peitzman AB. Current management of hepatic trauma. Surg Clin. 2010;90(4):775\u201385.","journal-title":"Surg Clin"},{"issue":"1","key":"759_CR7","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1186\/s13017-015-0031-8","volume":"10","author":"K Doklesti\u0107","year":"2015","unstructured":"Doklesti\u0107 K, Stefanovi\u0107 B, Gregori\u0107 P, Ivan\u010devi\u0107 N, Lon\u010dar Z, Jovanovi\u0107 B, Bumba\u0161irevi\u0107 V, Jeremi\u0107 V, Vujadinovi\u0107 ST, Stefanovi\u0107 B, et al. Surgical management of AAST grades III\u2013V hepatic trauma by damage control surgery with perihepatic packing and definitive hepatic repair\u2013single centre experience. World J Emerg Surg. 2015;10(1):34.","journal-title":"World J Emerg Surg"},{"issue":"1","key":"759_CR8","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s00068-017-0765-y","volume":"44","author":"J Barrie","year":"2018","unstructured":"Barrie J, Jamdar S, Iniguez MF, Bouamra O, Jenks T, Lecky F, O\u2019Reilly DA. Improved outcomes for hepatic trauma in England and Wales over a decade of trauma and hepatobiliary surgery centralisation. Eur J Trauma Emerg Surg. 2018;44(1):63\u201370.","journal-title":"Eur J Trauma Emerg Surg"},{"key":"759_CR9","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1007\/978-3-319-62054-1_24","volume-title":"Diagnostic imaging in polytrauma patients","author":"GL Buquicchio","year":"2018","unstructured":"Buquicchio GL, Cuneo G, Giannecchini S, Palliola R, Trinci M, Miele V. The follow-up of patients with abdominal injuries. In: Miele V, Trinci M, editors. Diagnostic imaging in polytrauma patients. Cham: Springer; 2018. p. 509\u201332."},{"issue":"6","key":"759_CR10","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1097\/00005373-199106000-00011","volume":"31","author":"MA Croce","year":"1991","unstructured":"Croce MA, Fabian TC, Kudsk KA, Baum SL, Payne LW, Mangiante EC, Britt LG. AAST organ injury scale: correlation of CT-graded liver injuries and operative findings. J Trauma. 1991;31(6):806\u201312.","journal-title":"J Trauma"},{"key":"759_CR11","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1007\/978-3-030-26710-0_86","volume-title":"Evidence-based critical care","author":"EC Gwinn","year":"2020","unstructured":"Gwinn EC, Park PK. Blunt abdominal trauma. In: Hyzy RC, McSparron J, editors. Evidence-based critical care. Cham: Springer; 2020. p. 651\u20138."},{"key":"759_CR12","unstructured":"https:\/\/www.aast.org\/resources-detail\/injury-scoring-scale. Accessed: July 2021."},{"issue":"8","key":"759_CR13","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1177\/000313481207800816","volume":"78","author":"WF Powers","year":"2012","unstructured":"Powers WF, Beard LN, Adams A, Kotwall CA, Clancy TV, Hope WW. Solid organ injury grading in trauma: accuracy of grading by surgical residents. Am Surg. 2012;78(8):834\u20136.","journal-title":"Am Surg"},{"issue":"06","key":"759_CR14","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1055\/s-0029-1241818","volume":"19","author":"DR Nellensteijn","year":"2009","unstructured":"Nellensteijn DR, Ten Duis HJ, Oldenziel J, Polak WG, Hulscher JBF. Only moderate intra-and inter-observer agreement between radiologists and surgeons when grading blunt paediatric hepatic injury on CT scan. Eur J Pediatr Surg. 2009;19(06):392\u20134.","journal-title":"Eur J Pediatr Surg"},{"issue":"2","key":"759_CR15","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1148\/radiol.2018171820","volume":"288","author":"G Choy","year":"2018","unstructured":"Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, Geis JR, Pandharipande PV, Brink JA, Dreyer KJ. Current applications and future impact of machine learning in radiology. Radiology. 2018;288(2):318\u201328.","journal-title":"Radiology"},{"issue":"6","key":"759_CR16","doi-asserted-by":"publisher","first-page":"2556","DOI":"10.1007\/s00261-020-02892-x","volume":"46","author":"D Dreizin","year":"2021","unstructured":"Dreizin D, Chen T, Liang Y, Zhou Y, Paes F, Wang Y, Yuille AL, Roth P, Champ K, Li G, et al. Added value of deep learning-based liver parenchymal ct volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis. Abdom Radiol. 2021;46(6):2556\u201366.","journal-title":"Abdom Radiol"},{"key":"759_CR17","doi-asserted-by":"publisher","first-page":"20585","DOI":"10.1109\/ACCESS.2019.2896961","volume":"7","author":"M Ahmad","year":"2019","unstructured":"Ahmad M, Ai D, Xie G, Qadri SF, Song H, Huang Y, Wang Y, Yang J. Deep belief network modeling for automatic liver segmentation. IEEE Access. 2019;7:20585\u201395.","journal-title":"IEEE Access"},{"issue":"2","key":"759_CR18","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s11548-016-1467-3","volume":"12","author":"F Lu","year":"2017","unstructured":"Lu F, Wu F, Hu P, Peng Z, Kong D. Automatic 3D liver location and segmentation via convolutional neural network and graph cut. Int J Comput Assist Radiol Surg. 2017;12(2):171\u201382.","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"759_CR19","doi-asserted-by":"crossref","unstructured":"Christ PF, Elshaer ME, Ettlinger F, Tatavarty S, Bickel M, Bilic P, Rempfler M, Armbruster M, Hofmann F, D\u2019Anastasi M, et al. Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. In International conference on medical image computing and computer-assisted intervention, pp. 415\u201323. 2016. Springer.","DOI":"10.1007\/978-3-319-46723-8_48"},{"key":"759_CR20","doi-asserted-by":"crossref","unstructured":"Farzaneh N, Samavi S, Soroushmehr SR, Patel H, Habbo-Gavin S, Fessell DP, Ward KR, Najarian K. Liver segmentation using location and intensity probabilistic atlases. In: 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp. 6453\u20136. 2016. IEEE.","DOI":"10.1109\/EMBC.2016.7592206"},{"key":"759_CR21","doi-asserted-by":"crossref","unstructured":"Farzaneh N, Habbo-Gavin S, Soroushmehr SR, Patel H, Fessell DP, Ward KR, Najarian K. Atlas based 3D liver segmentation using adaptive thresholding and superpixel approaches. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 1093\u20137. 2017. IEEE.","DOI":"10.1109\/ICASSP.2017.7952325"},{"key":"759_CR22","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.compbiomed.2019.04.014","volume":"110","author":"MA Lebre","year":"2019","unstructured":"Lebre MA, Vacavant A, Grand-Brochier M, Rositi H, Abergel A, Chabrot P, Magnin B. Automatic segmentation methods for liver and hepatic vessels from CT and MRI volumes, applied to the Couinaud scheme. Comput Biol Med. 2019;110:42\u201351.","journal-title":"Comput Biol Med"},{"key":"759_CR23","doi-asserted-by":"publisher","first-page":"101635","DOI":"10.1016\/j.compmedimag.2019.05.003","volume":"76","author":"MA Lebre","year":"2019","unstructured":"Lebre MA, Vacavant A, Grand-Brochier M, Rositi H, Strand R, Rosier H, Abergel A, Chabrot P, Magnin B. A robust multi-variability model based liver segmentation algorithm for CT-scan and MRI modalities. Comput Med Imaging Graph. 2019;76:101635.","journal-title":"Comput Med Imaging Graph"},{"issue":"11","key":"759_CR24","doi-asserted-by":"publisher","first-page":"1390","DOI":"10.1016\/j.acra.2008.07.008","volume":"15","author":"T Okada","year":"2008","unstructured":"Okada T, Shimada R, Hori M, Nakamoto M, Chen YW, Nakamura H, Sato Y. Automated segmentation of the liver from 3d ct images using probabilistic atlas and multilevel statistical shape model. Acad Radiol. 2008;15(11):1390\u2013403.","journal-title":"Acad Radiol"},{"key":"759_CR25","doi-asserted-by":"crossref","unstructured":"Rafiei S, Karimi N, Mirmahboub B, Najarian K, Felfeliyan B, Samavi S, Soroushmehr SR. Liver segmentation in abdominal CT images using probabilistic atlas and adaptive 3D region growing. In 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp. 6310\u20133. 2019. IEEE.","DOI":"10.1109\/EMBC.2019.8857835"},{"key":"759_CR26","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.media.2017.02.008","volume":"38","author":"C Shi","year":"2017","unstructured":"Shi C, Cheng Y, Wang J, Wang Y, Mori K, Tamura S. Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation. Med Image Anal. 2017;38:30\u201349.","journal-title":"Med Image Anal"},{"issue":"4","key":"759_CR27","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1177\/000313480006600403","volume":"66","author":"RF Cuff","year":"2000","unstructured":"Cuff RF, Cogbill TH, Lambert PJ, Lucas CE, et al. Nonoperative management of blunt liver trauma: the value of follow-up abdominal computed tomography scans\/discussion. Am Surg. 2000;66(4):332.","journal-title":"Am Surg"},{"key":"759_CR28","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp. 234\u201341. 2015. Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"1","key":"759_CR29","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/0149-936X(81)90054-0","volume":"5","author":"GM Bydder","year":"1981","unstructured":"Bydder GM, Chapman RW, Harry D, Bassan L, Sherlock S, Kreel L. Computed tomography attenuation values in fatty liver. J Comput Tomogr. 1981;5(1):33.","journal-title":"J Comput Tomogr"},{"issue":"1","key":"759_CR30","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s10620-005-1267-z","volume":"50","author":"DA Sass","year":"2005","unstructured":"Sass DA, Chang P, Chopra KB. Nonalcoholic fatty liver disease: a clinical review. Dig Dis Sci. 2005;50(1):171.","journal-title":"Dig Dis Sci"},{"issue":"4","key":"759_CR31","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1148\/rg.2016150207","volume":"36","author":"ML Wells","year":"2016","unstructured":"Wells ML, Fenstad ER, Poterucha JT, Hough DM, Young PM, Araoz PA, Ehman RL, Venkatesh SK. Imaging findings of congestive hepatopathy. Radiographics. 2016;36(4):1024\u201337.","journal-title":"Radiographics"},{"issue":"2","key":"759_CR32","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1109\/83.902291","volume":"10","author":"TF Chan","year":"2001","unstructured":"Chan TF, Vese LA. Active contours without edges. IEEE Trans Image Process. 2001;10(2):266\u201377.","journal-title":"IEEE Trans Image Process"},{"key":"759_CR33","doi-asserted-by":"crossref","unstructured":"Farzaneh N, Soroushmehr SR, Patel H, Wood A, Gryak J, Fessell D, Najarian K. Automated kidney segmentation for traumatic injured patients through ensemble learning and active contour modeling. In: 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp. 3418\u201321. 2018. IEEE.","DOI":"10.1109\/EMBC.2018.8512967"},{"issue":"3","key":"759_CR34","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1023\/A:1008036829907","volume":"29","author":"RT Whitaker","year":"1998","unstructured":"Whitaker RT. A level-set approach to 3d reconstruction from range data. Int J Comput Vis. 1998;29(3):203\u201331.","journal-title":"Int J Comput Vis"},{"issue":"8","key":"759_CR35","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1109\/TMI.2009.2013851","volume":"28","author":"T Heimann","year":"2009","unstructured":"Heimann T, Van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G, et al. Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging. 2009;28(8):1251\u201365.","journal-title":"IEEE Trans Med Imaging"},{"issue":"8476","key":"759_CR36","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/S0140-6736(86)90837-8","volume":"327","author":"JM Bland","year":"1986","unstructured":"Bland JM, Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. The Lancet. 1986;327(8476):307\u201310.","journal-title":"The Lancet"},{"issue":"8","key":"759_CR37","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1016\/j.ijnurstu.2009.10.001","volume":"47","author":"JM Bland","year":"2010","unstructured":"Bland JM, Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. Int J Nurs Stud. 2010;47(8):931\u20136.","journal-title":"Int J Nurs Stud"},{"key":"759_CR38","unstructured":"https:\/\/www.ircad.fr\/research\/3d-ircadb-01\/. Accessed: July 2021."},{"key":"759_CR39","unstructured":"Kavur AE, Kuncheva LI, Selver MA. Basic ensembles of vanilla-style deep learning models improve liver segmentation from CT images. 2020. arXiv preprint, arXiv:2001.09647."},{"key":"759_CR40","doi-asserted-by":"publisher","first-page":"68944","DOI":"10.1109\/ACCESS.2020.2985671","volume":"8","author":"XF Xi","year":"2020","unstructured":"Xi XF, Wang L, Sheng VS, Cui Z, Fu B, Hu F. Cascade u-resnets for simultaneous liver and lesion segmentation. IEEE Access. 2020;8:68944\u201352.","journal-title":"IEEE Access"},{"issue":"10","key":"759_CR41","doi-asserted-by":"publisher","first-page":"773","DOI":"10.3390\/diagnostics10100773","volume":"10","author":"N Farzaneh","year":"2020","unstructured":"Farzaneh N, Williamson CA, Jiang C, Srinivasan A, Bapuraj JR, Gryak J, Najarian K, Soroushmehr SM. Automated segmentation and severity analysis of subdural hematoma for patients with traumatic brain injuries. Diagnostics. 2020;10(10):773.","journal-title":"Diagnostics"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-022-00759-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-022-00759-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-022-00759-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T16:41:17Z","timestamp":1674924077000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-022-00759-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,8]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["759"],"URL":"https:\/\/doi.org\/10.1186\/s12880-022-00759-9","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,8]]},"assertion":[{"value":"25 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Institutional Review Board at the University of Michigan (HUM00098656) with a waiver of informed consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"All contributing authors are included in an invention disclosure with the University of Michigan\u2019s Office of Technology Transfer. Kayvan Najarian is a member of the editorial board for the BMC Medical Imaging journal. Besides, the authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"39"}}