{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T23:19:55Z","timestamp":1784762395737,"version":"3.55.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-024-67658-7","type":"journal-article","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T08:01:52Z","timestamp":1721721712000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["AI-based segmentation of renal enhanced\u00a0CT images for quantitative evaluate of chronic kidney disease"],"prefix":"10.1038","volume":"14","author":[{"given":"Hui","family":"Luo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingzhen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianghong","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyuan","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yibo","family":"Ying","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"issue":"2","key":"67658_CR1","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1159\/000496160","volume":"49","author":"K Jiang","year":"2019","unstructured":"Jiang, K. & Lerman, L. O. Prediction of chronic kidney disease progression by magnetic resonance imaging: Where are we?. Am. J. Nephrol. 49(2), 111\u2013113 (2019).","journal-title":"Am. J. Nephrol."},{"issue":"19","key":"67658_CR2","doi-asserted-by":"publisher","first-page":"1833","DOI":"10.1056\/NEJMra0902710","volume":"363","author":"J Himmelfarb","year":"2010","unstructured":"Himmelfarb, J. & Ikizler, T. A. Hemodialysis. N. Engl. J. Med. 363(19), 1833\u20131845 (2010).","journal-title":"N. Engl. J. Med."},{"issue":"2 Suppl 1","key":"67658_CR3","first-page":"S1","volume":"39","author":"K\/DOQI clinical practice guidelines for chronic kidney disease","year":"2002","unstructured":"K\/DOQI clinical practice guidelines for chronic kidney disease. evaluation, classification, and stratification. Am. J. Kidney Dis. 39(2 Suppl 1), S1\u2013S266 (2002).","journal-title":"Am. J. Kidney Dis."},{"issue":"2","key":"67658_CR4","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/s00467-018-4067-3","volume":"34","author":"CL Salvador","year":"2019","unstructured":"Salvador, C. L. et al. Estimating glomerular filtration rate in children: Evaluation of creatinine- and cystatin C-based equations. Pediatr. Nephrol. 34(2), 301\u2013311 (2019).","journal-title":"Pediatr. Nephrol."},{"issue":"4","key":"67658_CR5","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer, E., Long, J. & Darrell, T. Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640\u2013651 (2017).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"67658_CR6","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1002\/jmri.22640","volume":"34","author":"M Notohamiprodjo","year":"2011","unstructured":"Notohamiprodjo, M. et al. Comparison of Gd-DTPA and Gd-BOPTA for studying renal perfusion and filtration. J. Magn. Reson. Imaging 34(3), 595\u2013607 (2011).","journal-title":"J. Magn. Reson. Imaging"},{"issue":"1","key":"67658_CR7","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1681\/ASN.2005070757","volume":"17","author":"M Nangaku","year":"2006","unstructured":"Nangaku, M. Chronic hypoxia and tubulointerstitial injury: A final common pathway to end-stage renal failure. J. Am. Soc. Nephrol. 17(1), 17\u201325 (2006).","journal-title":"J. Am. Soc. Nephrol."},{"issue":"5","key":"67658_CR8","doi-asserted-by":"publisher","first-page":"2293","DOI":"10.1007\/s00330-018-5911-3","volume":"29","author":"J Ding","year":"2019","unstructured":"Ding, J. et al. Evaluation of renal dysfunction using texture analysis based on DWI, BOLD, and susceptibility-weighted imaging. Eur. Radiol. 29(5), 2293\u20132301 (2019).","journal-title":"Eur. Radiol."},{"issue":"2","key":"67658_CR9","first-page":"157","volume":"11","author":"AA Abbasian","year":"2017","unstructured":"Abbasian, A. A. et al. Assessment of kidney function after allograft transplantation by texture analysis. Iran J. Kidney Dis. 11(2), 157\u2013164 (2017).","journal-title":"Iran J. Kidney Dis."},{"issue":"9","key":"67658_CR10","doi-asserted-by":"publisher","first-page":"604","DOI":"10.7326\/0003-4819-150-9-200905050-00006","volume":"150","author":"AS Levey","year":"2009","unstructured":"Levey, A. S. et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 150(9), 604\u2013612 (2009).","journal-title":"Ann. Intern. Med."},{"key":"67658_CR11","doi-asserted-by":"publisher","first-page":"4189781","DOI":"10.1155\/2022\/4189781","volume":"2022","author":"XX Yin","year":"2022","unstructured":"Yin, X. X. et al. U-Net-based medical image segmentation. J. Healthc. Eng. 2022, 4189781 (2022).","journal-title":"J. Healthc. Eng."},{"issue":"23","key":"67658_CR12","doi-asserted-by":"publisher","first-page":"2473","DOI":"10.1056\/NEJMra054415","volume":"354","author":"LA Stevens","year":"2006","unstructured":"Stevens, L. A. et al. Assessing kidney function\u2013measured and estimated glomerular filtration rate. N. Engl. J. Med. 354(23), 2473\u20132483 (2006).","journal-title":"N. Engl. J. Med."},{"issue":"1","key":"67658_CR13","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1159\/000180580","volume":"16","author":"DW Cockcroft","year":"1976","unstructured":"Cockcroft, D. W. & Gault, M. H. Prediction of creatinine clearance from serum creatinine. Nephron 16(1), 31\u201341 (1976).","journal-title":"Nephron"},{"issue":"6","key":"67658_CR14","doi-asserted-by":"publisher","first-page":"461","DOI":"10.7326\/0003-4819-130-6-199903160-00002","volume":"130","author":"AS Levey","year":"1999","unstructured":"Levey, A. S. et al. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann. Intern. Med. 130(6), 461\u2013470 (1999).","journal-title":"Ann. Intern. Med."},{"issue":"5","key":"67658_CR15","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1049\/syb2.12031","volume":"15","author":"H Peng","year":"2021","unstructured":"Peng, H. et al. A two-stage neural network prediction of chronic kidney disease. IET Syst. Biol. 15(5), 163\u2013171 (2021).","journal-title":"IET Syst. Biol."},{"key":"67658_CR16","doi-asserted-by":"publisher","first-page":"102109","DOI":"10.1016\/j.artmed.2021.102109","volume":"117","author":"PH Conze","year":"2021","unstructured":"Conze, P. H. et al. Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks. Artif. Intell. Med. 117, 102109 (2021).","journal-title":"Artif. Intell. Med."},{"issue":"Suppl 9","key":"67658_CR17","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1186\/s12911-019-0988-4","volume":"19","author":"V Bevilacqua","year":"2019","unstructured":"Bevilacqua, V. et al. A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images. BMC Med. Inform. Decis. Mak. 19(Suppl 9), 244 (2019).","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"67658_CR18","first-page":"1741","volume":"2019","author":"S Yin","year":"2019","unstructured":"Yin, S. et al. fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network. Proc. IEEE Int. Symp. Biomed. Imaging 2019, 1741\u20131744 (2019).","journal-title":"Proc. IEEE Int. Symp. Biomed. Imaging"},{"issue":"1","key":"67658_CR19","doi-asserted-by":"publisher","first-page":"2049","DOI":"10.1038\/s41598-017-01779-0","volume":"7","author":"K Sharma","year":"2017","unstructured":"Sharma, K. et al. Automatic segmentation of kidneys using deep learning for total kidney volume quantification in autosomal dominant polycystic kidney disease. Sci. Rep. 7(1), 2049 (2017).","journal-title":"Sci. Rep."},{"key":"67658_CR20","doi-asserted-by":"publisher","first-page":"103906","DOI":"10.1016\/j.compbiomed.2020.103906","volume":"123","author":"CL Da","year":"2020","unstructured":"Da, C. L. et al. Kidney segmentation from computed tomography images using deep neural network. Comput. Biol. Med. 123, 103906 (2020).","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"67658_CR21","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1681\/ASN.2021030404","volume":"33","author":"P Korfiatis","year":"2022","unstructured":"Korfiatis, P. et al. Automated segmentation of kidney cortex and medulla in CT images: A multisite evaluation study. J. Am. Soc. Nephrol. 33(2), 420\u2013430 (2022).","journal-title":"J. Am. Soc. Nephrol."},{"issue":"7","key":"67658_CR22","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1016\/j.acra.2017.11.015","volume":"25","author":"D Turco","year":"2018","unstructured":"Turco, D. et al. Fully automated segmentation of polycystic kidneys from noncontrast computed tomography: A feasibility study and preliminary results. Acad. Radiol. 25(7), 850\u2013855 (2018).","journal-title":"Acad. Radiol."},{"issue":"5","key":"67658_CR23","doi-asserted-by":"publisher","first-page":"e230024","DOI":"10.1148\/ryai.230024","volume":"5","author":"J Wasserthal","year":"2023","unstructured":"Wasserthal, J. et al. Totalsegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol. Artif. Intell. 5(5), e230024 (2023).","journal-title":"Radiol. Artif. Intell."},{"issue":"2","key":"67658_CR24","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/s10157-017-1454-1","volume":"22","author":"Y Mitsui","year":"2018","unstructured":"Mitsui, Y. et al. The assessment of renal cortex and parenchymal volume using automated CT volumetry for predicting renal function after donor nephrectomy. Clin. Exp. Nephrol. 22(2), 453\u2013458 (2018).","journal-title":"Clin. Exp. Nephrol."},{"issue":"6","key":"67658_CR25","doi-asserted-by":"publisher","first-page":"1270","DOI":"10.1097\/TP.0000000000000889","volume":"100","author":"R Wahba","year":"2016","unstructured":"Wahba, R. et al. Computed tomography volumetry in preoperative living kidney donor assessment for prediction of split renal function. Transplantation 100(6), 1270\u20131277 (2016).","journal-title":"Transplantation"},{"key":"67658_CR26","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.ejrad.2018.04.016","volume":"103","author":"C Houbois","year":"2018","unstructured":"Houbois, C. et al. Can computed tomography volumetry of the renal cortex replace MAG3-scintigraphy in all patients for determining split renal function?. Eur. J. Radiol. 103, 105\u2013111 (2018).","journal-title":"Eur. J. Radiol."},{"key":"67658_CR27","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1038\/s41746-019-0104-2","volume":"2","author":"CC Kuo","year":"2019","unstructured":"Kuo, C. C. et al. Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning. NPJ Digit. Med. 2, 29 (2019).","journal-title":"NPJ Digit. Med."},{"issue":"4","key":"67658_CR28","first-page":"4997","volume":"15","author":"H Chang","year":"2018","unstructured":"Chang, H. et al. Drug distribution and clinical safety in treating cystic craniopharyngiomas using intracavitary radiotherapy with phosphorus-32 colloid. Oncol. Lett. 15(4), 4997\u20135003 (2018).","journal-title":"Oncol. Lett."},{"issue":"1","key":"67658_CR29","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1159\/000504708","volume":"6","author":"J Zhang","year":"2020","unstructured":"Zhang, J. & Zhang, L. J. Functional MRI as a tool for evaluating interstitial fibrosis and prognosis in kidney disease. Kidney Dis. (Basel) 6(1), 7\u201312 (2020).","journal-title":"Kidney Dis. (Basel)"},{"issue":"4","key":"67658_CR30","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.21037\/qims-20-842","volume":"11","author":"G Zhang","year":"2021","unstructured":"Zhang, G. et al. Texture analysis based on quantitative magnetic resonance imaging to assess kidney function: A preliminary study. Quant. Imaging Med. Surg. 11(4), 1256\u20131270 (2021).","journal-title":"Quant. Imaging Med. Surg."},{"issue":"5","key":"67658_CR31","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1007\/s10278-022-00625-8","volume":"35","author":"S Lee","year":"2022","unstructured":"Lee, S. et al. Machine learning-aided chronic kidney disease diagnosis based on ultrasound imaging integrated with computer-extracted measurable features. J. Digit. Imaging 35(5), 1091\u20131100 (2022).","journal-title":"J. Digit. Imaging"},{"issue":"1","key":"67658_CR32","doi-asserted-by":"publisher","first-page":"83","DOI":"10.34067\/KID.0003662021","volume":"3","author":"C Chantaduly","year":"2022","unstructured":"Chantaduly, C. et al. Artificial intelligence assessment of renal scarring (AIRS study). Kidney360 3(1), 83\u201390 (2022).","journal-title":"Kidney360"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-67658-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-67658-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-67658-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T08:09:03Z","timestamp":1721722143000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-67658-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,23]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["67658"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-67658-7","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,23]]},"assertion":[{"value":"2 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"16890"}}