{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T14:12:28Z","timestamp":1777126348991,"version":"3.51.4"},"reference-count":32,"publisher":"The Company of Biologists","issue":"9","license":[{"start":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T00:00:00Z","timestamp":1695254400000},"content-version":"vor","delay-in-days":6,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000836","name":"University of Liverpool","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000836","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,15]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:p>This study focuses on ischaemia-reperfusion injury (IRI) in kidneys, a cause of acute kidney injury (AKI) and end-stage kidney disease (ESKD). Traditional kidney damage assessment methods are semi-quantitative and subjective. This study aims to use a convolutional neural network (CNN) to segment murine kidney structures after IRI, quantify damage via CNN-generated pathological measurements, and compare this to conventional scoring. The CNN was able to accurately segment the different pathological classes, such as Intratubular casts and Tubular necrosis, with an F1 score of over 0.75. Some classes, such as Glomeruli and Proximal tubules, had even higher statistical values with F1 scores over 0.90. The scoring generated based on the segmentation approach statistically correlated with the semiquantitative assessment (Spearman\u2019s rank correlation coefficient=0.94). The heatmap approach localised the intratubular necrosis mainly in the outer stripe of the outer medulla, while the tubular casts were also present in more superficial or deeper portions of the cortex and medullary areas. This study presents a CNN model capable of segmenting multiple classes of interest, including acute IRI-specific pathological changes, in a whole mouse kidney section and can provide insights into the distribution of pathological classes within the whole mouse kidney section.<\/jats:p>","DOI":"10.1242\/bio.059988","type":"journal-article","created":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T12:03:51Z","timestamp":1693310631000},"source":"Crossref","is-referenced-by-count":8,"title":["Quantifying acute kidney injury in an Ischaemia-Reperfusion Injury mouse model using deep-learning-based semantic segmentation in histology"],"prefix":"10.1242","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7959-6190","authenticated-orcid":false,"given":"Andreea","family":"Luchian","sequence":"first","affiliation":[{"name":"Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health & Life Sciences, University of Liverpool 1 Department of Veterinary Anatomy Physiology and Pathology , , Liverpool, CH64 7TE , UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5830-351X","authenticated-orcid":false,"given":"Katherine Trivino","family":"Cepeda","sequence":"additional","affiliation":[{"name":"Institute of Systems, Molecular and Integrative Biology, University of Liverpool 2 Department of Molecular Physiology and Cell Signalling , , Liverpool, L69 7BE , UK"},{"name":"Centre for Pre-clinical Imaging, Institute of Systems, Molecular and Integrative Biology, University of Liverpool 3 , Liverpool, L69 7TX , UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3440-3142","authenticated-orcid":false,"given":"Rachel","family":"Harwood","sequence":"additional","affiliation":[{"name":"Alder Hey in the Park 4 Department of Paediatric Surgery , , Liverpool, L14 5AB , UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1316-148X","authenticated-orcid":false,"given":"Patricia","family":"Murray","sequence":"additional","affiliation":[{"name":"Institute of Systems, Molecular and Integrative Biology, University of Liverpool 2 Department of Molecular Physiology and Cell Signalling , , Liverpool, L69 7BE , UK"},{"name":"Centre for Pre-clinical Imaging, Institute of Systems, Molecular and Integrative Biology, University of Liverpool 3 , Liverpool, L69 7TX , UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9245-993X","authenticated-orcid":false,"given":"Bettina","family":"Wilm","sequence":"additional","affiliation":[{"name":"Institute of Systems, Molecular and Integrative Biology, University of Liverpool 2 Department of Molecular Physiology and Cell Signalling , , Liverpool, L69 7BE , UK"},{"name":"Centre for Pre-clinical Imaging, Institute of Systems, Molecular and Integrative Biology, University of Liverpool 3 , Liverpool, L69 7TX , UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5917-8554","authenticated-orcid":false,"given":"Simon","family":"Kenny","sequence":"additional","affiliation":[{"name":"Alder Hey in the Park 4 Department of Paediatric Surgery , , Liverpool, L14 5AB , UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1509-5108","authenticated-orcid":false,"given":"Paola","family":"Pregel","sequence":"additional","affiliation":[{"name":"University of Turin 5 Department of Veterinary Sciences , , Turin, 8-10124 , Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6614-1223","authenticated-orcid":false,"given":"Lorenzo","family":"Ressel","sequence":"additional","affiliation":[{"name":"Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health & Life Sciences, University of Liverpool 1 Department of Veterinary Anatomy Physiology and Pathology , , Liverpool, CH64 7TE , UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"237","published-online":{"date-parts":[[2023,9,21]]},"reference":[{"key":"2023092113054586800_BIO059988C1","doi-asserted-by":"publisher","first-page":"6047","DOI":"10.1038\/s41598-020-62960-6","article-title":"Digital image analysis of heterogeneous tuberculosis pulmonary pathology in non-clinical animal models using deep convolutional neural networks","volume":"10","author":"Asay","year":"2020","journal-title":"Sci. Rep."},{"key":"2023092113054586800_BIO059988C2","doi-asserted-by":"publisher","first-page":"2081","DOI":"10.1681\/ASN.2017111210","article-title":"Region-based convolutional neural nets for localization of glomeruli in trichrome-stained whole kidney sections","volume":"29","author":"Bukowy","year":"2018","journal-title":"JASN"},{"key":"2023092113054586800_BIO059988C3","doi-asserted-by":"publisher","first-page":"46450","DOI":"10.1038\/srep46450","article-title":"Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent","volume":"7","author":"Cruz-Roa","year":"2017","journal-title":"Sci. Rep."},{"key":"2023092113054586800_BIO059988C4","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1053\/j.semdp.2020.02.001","article-title":"A practical approach to the pathology of renal intratubular casts. Seminars in diagnostic pathology","volume":"37","author":"Dvanajscak","year":"2020","journal-title":"Practical Topics and Updates in Renal Pathology"},{"key":"2023092113054586800_BIO059988C5","doi-asserted-by":"publisher","first-page":"14S","DOI":"10.1177\/0192623312438736","article-title":"Proliferative and nonproliferative lesions of the rat and mouse urinary system","volume":"40","author":"Frazier","year":"2012","journal-title":"Toxicol. Pathol."},{"key":"2023092113054586800_BIO059988C6","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.compmedimag.2018.11.002","article-title":"CNN cascades for segmenting sparse objects in gigapixel whole slide images","volume":"71","author":"Gadermayr","year":"2019","journal-title":"Comput. Med. Imaging Graph."},{"key":"2023092113054586800_BIO059988C7","doi-asserted-by":"publisher","first-page":"20","DOI":"10.3390\/jimaging4010020","article-title":"Glomerulus classification and detection based on convolutional neural networks","volume":"4","author":"Gallego","year":"2018","journal-title":"J. Imaging"},{"key":"2023092113054586800_BIO059988C8","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1681\/ASN.2020050652","article-title":"Automated computational detection of interstitial fibrosis, tubular atrophy, and glomerulosclerosis","volume":"32","author":"Ginley","year":"2021","journal-title":"J. Am. Soc. Nephrol."},{"key":"2023092113054586800_BIO059988C9","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.1097\/01.ASN.0000066143.02832.5E","article-title":"Computerized image analysis of sirius red\u2013stained renal allograft biopsies as a surrogate marker to predict long-term allograft function","volume":"14","author":"Grimm","year":"2003","journal-title":"JASN"},{"key":"2023092113054586800_BIO059988C10","doi-asserted-by":"publisher","first-page":"e15211","DOI":"10.1101\/2019.12.17.879742","article-title":"Murine models of renal ischaemia reperfusion injury: An opportunity for refinement using non-invasive monitoring methods (preprint)","volume":"10","author":"Harwood","year":"2019","journal-title":"Physiology"},{"key":"2023092113054586800_BIO059988C11","doi-asserted-by":"publisher","first-page":"e0202708","DOI":"10.1371\/journal.pone.0202708","article-title":"Deep neural network based histological scoring of lung fibrosis and inflammation in the mouse model system","volume":"13","author":"Heinemann","year":"2018","journal-title":"PLoS One"},{"key":"2023092113054586800_BIO059988C12","doi-asserted-by":"publisher","first-page":"1968","DOI":"10.1681\/ASN.2019020144","article-title":"Deep learning\u2013based histopathologic assessment of kidney tissue","volume":"30","author":"Hermsen","year":"2019","journal-title":"JASN"},{"key":"2023092113054586800_BIO059988C13","first-page":"doi: 10.3791\/51816","article-title":"Renal ischaemia reperfusion injury: a mouse model of injury and regeneration","volume":"88","author":"Hesketh","year":"2014","journal-title":"JoVE"},{"key":"2023092113054586800_BIO059988C14","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1038\/s41467-023-36173-0","article-title":"Next-Generation Morphometry for pathomics-data mining in histopathology","volume":"14","author":"H\u00f6lscher","year":"2023","journal-title":"Nat. Commun."},{"key":"2023092113054586800_BIO059988C15","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.kint.2020.07.044","article-title":"Development and evaluation of deep learning\u2013based segmentation of histologic structures in the kidney cortex with multiple histologic stains","volume":"99","author":"Jayapandian","year":"2021","journal-title":"Kidney Int."},{"key":"2023092113054586800_BIO059988C16","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1186\/s12859-015-0739-1","article-title":"Segmental HOG: new descriptor for glomerulus detection in kidney microscopy image","volume":"16","author":"Kato","year":"2015","journal-title":"BMC Bioinformatics"},{"key":"2023092113054586800_BIO059988C17","author":"Kshirsagar","year":"2021"},{"key":"2023092113054586800_BIO059988C18","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/S0140-6736(13)60647-9","article-title":"Acute kidney injury: an increasing global concern","volume":"382","author":"Lameire","year":"2013","journal-title":"The Lancet"},{"key":"2023092113054586800_BIO059988C19","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1162\/jocn_a_01544","article-title":"Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future","volume":"33","author":"Lindsay","year":"2021","journal-title":"J. Cogn. Neurosci."},{"key":"2023092113054586800_BIO059988C20","doi-asserted-by":"publisher","first-page":"260","DOI":"10.2215\/CJN.07830621","article-title":"Automatic evaluation of histological prognostic factors using two consecutive convolutional neural networks on kidney samples","volume":"17","author":"Marechal","year":"2022","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"2023092113054586800_BIO059988C21","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1078\/0940-2993-00350","article-title":"Revised guides for organ sampling and trimming in rats and mice \u2013 Part 3","volume":"55","author":"Morawietz","year":"2004","journal-title":"Exp. Toxicol. Pathol."},{"key":"2023092113054586800_BIO059988C22","doi-asserted-by":"publisher","first-page":"e0195621","DOI":"10.1371\/journal.pone.0195621","article-title":"Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning","volume":"13","author":"Niazi","year":"2018","journal-title":"PLoS One"},{"key":"2023092113054586800_BIO059988C23","doi-asserted-by":"publisher","first-page":"e253","DOI":"10.1016\/S1470-2045(19)30154-8","article-title":"Digital Pathology and Artificial Intelligence","volume":"20","author":"Niazi","year":"2019","journal-title":"Lancet Oncol."},{"key":"2023092113054586800_BIO059988C24","first-page":"128","article-title":". Deep learning vs. traditional computer vision","volume-title":"Advances in Computer Vision, Advances in Intelligent Systems and Computing","author":"O'Mahony","year":"2020"},{"key":"2023092113054586800_BIO059988C25","doi-asserted-by":"crossref","DOI":"10.1002\/9780470344613","volume-title":"Pathology of Laboratory Rodents and Rabbits","author":"Percy","year":"2007","edition":"3rd edn"},{"key":"2023092113054586800_BIO059988C26","author":"Ronneberger","year":"2015"},{"key":"2023092113054586800_BIO059988C27","doi-asserted-by":"publisher","author":"Scudamore","year":"2013","DOI":"10.1002\/9781118789568"},{"key":"2023092113054586800_BIO059988C28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/7386954","article-title":"A noninvasive imaging toolbox indicates limited therapeutic potential of conditionally activated macrophages in a mouse model of multiple organ dysfunction","volume":"2019","author":"Sharkey","year":"2019","journal-title":"Stem Cells Int."},{"key":"2023092113054586800_BIO059988C29","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s12967-016-0782-3","article-title":"Human endometrial regenerative cells attenuate renal ischemia reperfusion injury in mice","volume":"14","author":"Sun","year":"2016","journal-title":"J. Transl. Med."},{"key":"2023092113054586800_BIO059988C30","doi-asserted-by":"publisher","first-page":"1902761","DOI":"10.1002\/adma.201902761","article-title":"Bridging biological and artificial neural networks with emerging neuromorphic devices: fundamentals, progress, and challenges","volume":"31","author":"Tang","year":"2019","journal-title":"Adv. Mater."},{"key":"2023092113054586800_BIO059988C31","doi-asserted-by":"publisher","first-page":"F997","DOI":"10.1152\/ajprenal.00130.2004","article-title":"Endotoxemic acute renal failure is attenuated in caspase-1-deficient mice","volume":"288","author":"Wang","year":"2005","journal-title":"Am. J. Physiol. Renal Physiol."},{"key":"2023092113054586800_BIO059988C32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1159\/000504600","article-title":"artificial intelligence in nephrology: how can artificial intelligence augment nephrologists\u2019 intelligence?","volume":"6","author":"Xie","year":"2020","journal-title":"Kidney Dis. (Basel)"}],"container-title":["Biology Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.logists.com\/bio\/bio\/article-pdf\/doi\/10.1242\/bio.059988\/3113973\/bio059988.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/journals.logists.com\/bio\/bio\/article-pdf\/doi\/10.1242\/bio.059988\/3161695\/bio059988.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/journals.logists.com\/bio\/bio\/article-pdf\/doi\/10.1242\/bio.059988\/3161695\/bio059988.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T13:06:22Z","timestamp":1695301582000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.biologists.com\/bio\/article\/12\/9\/bio059988\/329059\/Quantifying-acute-kidney-injury-in-an-Ischaemia"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,15]]},"references-count":32,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023,9,15]]}},"URL":"https:\/\/doi.org\/10.1242\/bio.059988","relation":{},"ISSN":["2046-6390"],"issn-type":[{"value":"2046-6390","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,15]]},"article-number":"bio059988"}}