{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T02:35:25Z","timestamp":1790822125612,"version":"4.1.0"},"reference-count":32,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1016\/j.cmpb.2025.108842","type":"journal-article","created":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T17:28:04Z","timestamp":1746725284000},"page":"108842","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"special_numbering":"C","title":["AI-driven glomerular morphology quantification: a novel pipeline for assessing basement membrane thickness and podocyte foot process effacement in kidney diseases"],"prefix":"10.1016","volume":"268","author":[{"given":"Michifumi","family":"Yamashita","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natalia","family":"Piaseczna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akira","family":"Takahashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3761-8491","authenticated-orcid":false,"given":"Daisuke","family":"Kiyozawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Narihito","family":"Tatsumoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2064-8907","authenticated-orcid":false,"given":"Shohei","family":"Kaneko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5080-242X","authenticated-orcid":false,"given":"Natalia","family":"Zurek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3107-602X","authenticated-orcid":false,"given":"Arkadiusz","family":"Gertych","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cmpb.2025.108842_bib0001","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1038\/ki.2013.364","article-title":"The clinical and immunological features of patients with combined anti-glomerular basement membrane disease and membranous nephropathy","volume":"85","author":"Jia","year":"2014","journal-title":"Kidney Int."},{"key":"10.1016\/j.cmpb.2025.108842_bib0002","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/s12882-021-02634-1","article-title":"A comparative study of human glomerular basement membrane thickness using direct measurement and orthogonal intercept methods","volume":"23","author":"Viana","year":"2022","journal-title":"BMC. Nephrol."},{"key":"10.1016\/j.cmpb.2025.108842_bib0003","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1038\/modpathol.3880461","article-title":"The value of electron microscopy in the diagnosis of chronic renal allograft rejection","volume":"14","author":"Ivanyi","year":"2001","journal-title":"Mod. Pathol."},{"key":"10.1016\/j.cmpb.2025.108842_bib0004","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1159\/000516831","article-title":"The continuing need for electron microscopy in examination of medical renal biopsies: Examples in practice","volume":"1","author":"Yamashita","year":"2021","journal-title":"Glomerular. Dis."},{"key":"10.1016\/j.cmpb.2025.108842_bib0005","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1681\/ASN.2014030278","article-title":"A potential role for mechanical forces in the detachment of podocytes and the progression of CKD","volume":"26","author":"Kriz","year":"2015","journal-title":"J. Am. Soc. Nephrol."},{"key":"10.1016\/j.cmpb.2025.108842_bib0006","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1038\/ki.1982.82","article-title":"Development of glomerular lesions in experimental long-term diabetes in the rat","volume":"21","author":"Hirose","year":"1982","journal-title":"Kidney Int."},{"key":"10.1016\/j.cmpb.2025.108842_bib0007","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1111\/j.1365-2818.1979.tb00149.x","article-title":"Determination of membrane thickness distribution from orthogonal intercepts","volume":"115","author":"Jensen","year":"1979","journal-title":"J. Microsc."},{"key":"10.1016\/j.cmpb.2025.108842_bib0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105557","article-title":"Segmentation and thickness calculation of glomerular basement membrane using rads-net in glomerular microscopic images","volume":"88","author":"Wang","year":"2024","journal-title":"Biomed. Signal. Process. Control"},{"key":"10.1016\/j.cmpb.2025.108842_bib0009","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.1016\/j.ekir.2019.06.011","article-title":"A systematic review of interpathologist agreement in histologic classification of lupus nephritis","volume":"4","author":"Dasari","year":"2019","journal-title":"Kidney Int. Rep."},{"key":"10.1016\/j.cmpb.2025.108842_bib0010","doi-asserted-by":"crossref","first-page":"25657","DOI":"10.1109\/ACCESS.2024.3365507","article-title":"Deformable R-CNN for detection of electron dense deposits in glomerular transmission electron microscopy images","volume":"12","author":"Liu","year":"2024","journal-title":"IEEe Access."},{"key":"10.1016\/j.cmpb.2025.108842_bib0011","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.kint.2023.09.011","article-title":"Development of an automated estimation of foot process width using deep learning in kidney biopsies from patients with fabry, minimal change, and diabetic kidney diseases","volume":"105","author":"Smerkous","year":"2024","journal-title":"Kidney Int."},{"key":"10.1016\/j.cmpb.2025.108842_bib0012","article-title":"Digital pathology assessment of kidney glomerular filtration barrier ultrastructure in an animal model of podocytopathy","author":"Laudon","year":"2024","journal-title":"bioRxiv."},{"key":"10.1016\/j.cmpb.2025.108842_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2023.102720","article-title":"GCLR: a self-supervised representation learning pretext task for glomerular filtration barrier segmentation in tem images","volume":"146","author":"Lin","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.cmpb.2025.108842_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106210","article-title":"Medical image recognition and segmentation of pathological slices of gastric cancer based on DeepLabv3+ neural network","volume":"207","author":"Wang","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.cmpb.2025.108842_bib0015","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1038\/modpathol.3800239","article-title":"Ad Hoc Committee on Renal Biopsy Guidelines of the Renal Pathology Society, Practice guidelines for the renal biopsy","volume":"17","author":"Walker","year":"2004","journal-title":"Mod. Pathol."},{"issue":"Pt 2","key":"10.1016\/j.cmpb.2025.108842_bib0016","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.compmedimag.2015.08.002","article-title":"Machine learning approaches to analyze histological images of tissues from radical prostatectomies","volume":"46","author":"Gertych","year":"2015","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.cmpb.2025.108842_bib0017","series-title":"Proceedings of the European conference on computer vision (ECCV)","first-page":"801","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"Chen","year":"2018"},{"key":"10.1016\/j.cmpb.2025.108842_bib0018","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1038\/s43018-023-00697-7","article-title":"The molecular twin artificial-intelligence platform integrates multi-omic data to predict outcomes for pancreatic adenocarcinoma patients","volume":"5","author":"Osipov","year":"2024","journal-title":"Nat. Cancer"},{"key":"10.1016\/j.cmpb.2025.108842_bib0019","doi-asserted-by":"crossref","first-page":"14013","DOI":"10.1007\/s13369-022-06608-9","article-title":"Glomerulus semantic segmentation using ensemble of deep learning models","volume":"47","author":"Gu","year":"2022","journal-title":"Arab. J. Sci. Eng."},{"key":"10.1016\/j.cmpb.2025.108842_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.ajpath.2025.01.009","article-title":"Tumor cellularity assessment using AI trained on IHC-restained slides improves selection of lung adenocarcinoma samples for molecular testing","author":"Gertych","year":"2025","journal-title":"Am. J. Pathol."},{"key":"10.1016\/j.cmpb.2025.108842_bib0021","unstructured":"D. Kingma, J. Ba, Adam: a method for stochastic optimization, International Conference on Learning Representations (2014). https:\/\/doi.org\/10.48550\/arXiv.1412.6980."},{"key":"10.1016\/j.cmpb.2025.108842_bib0022","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"10.1016\/j.cmpb.2025.108842_bib0023","doi-asserted-by":"crossref","first-page":"5068","DOI":"10.1038\/s41598-020-61808-3","article-title":"Test-time augmentation for deep learning-based cell segmentation on microscopy images","volume":"10","author":"Moshkov","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cmpb.2025.108842_bib0024","doi-asserted-by":"crossref","unstructured":"Z. Swiderska-Chadaj, Z. Ma, N. Ing, T. Markiewicz, M. Lorent, S. Cierniak, A.E. Walts, B.S. Knudsen, A. Gertych, Contextual classification of tumor growth patterns in digital histology slides, in: information Technology in Biomedicine, 2019, pp. 13\u201325. https:\/\/doi.org\/10.1007\/978-3-030-23762-2_2.","DOI":"10.1007\/978-3-030-23762-2_2"},{"key":"10.1016\/j.cmpb.2025.108842_bib0025","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","article-title":"A guideline of selecting and reporting intraclass correlation coefficients for reliability research","volume":"15","author":"Koo","year":"2016","journal-title":"J. Chiropr. Med."},{"key":"10.1016\/j.cmpb.2025.108842_bib0026","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1038\/ki.2008.413","article-title":"Podocyte foot process effacement as a diagnostic tool in focal segmental glomerulosclerosis","volume":"74","author":"Deegens","year":"2008","journal-title":"Kidney Int."},{"key":"10.1016\/j.cmpb.2025.108842_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108465","article-title":"AI explainability and bias propagation in medical decision support","volume":"257","author":"Gertych","year":"2024","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.cmpb.2025.108842_bib0028","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1038\/modpathol.3800219","article-title":"Isolate diffuse thickening of glomerular capillary basement membrane: a renal lesion in prediabetes?","volume":"17","author":"Mac-Moune Lai","year":"2004","journal-title":"Mod. Pathol."},{"key":"10.1016\/j.cmpb.2025.108842_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2021.101865","article-title":"A U-Net based framework to quantify glomerulosclerosis in digitized pas and H&E stained human tissues","volume":"89","author":"Gallego","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.cmpb.2025.108842_bib0030","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1093\/ndt\/gfz136","article-title":"Imaging the kidney: From light to super-resolution microscopy","volume":"36","author":"Angelotti","year":"2021","journal-title":"Nephrol. Dial. TransPlant"},{"key":"10.1016\/j.cmpb.2025.108842_bib0031","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.1001\/jama.2019.14745","article-title":"Chronic kidney disease diagnosis and management: a review","volume":"322","author":"Chen","year":"2019","journal-title":"JAMa"},{"key":"10.1016\/j.cmpb.2025.108842_bib0032","doi-asserted-by":"crossref","first-page":"364","DOI":"10.3390\/math11020364","article-title":"DeepLabv3+-based segmentation and best features selection using slime mould algorithm for multi-class skin lesion classification","volume":"11","author":"Zafar","year":"2023","journal-title":"Mathematics"}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260725002597?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260725002597?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T00:40:44Z","timestamp":1749084044000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260725002597"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":32,"alternative-id":["S0169260725002597"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2025.108842","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2025,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"AI-driven glomerular morphology quantification: a novel pipeline for assessing basement membrane thickness and podocyte foot process effacement in kidney diseases","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2025.108842","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"108842"}}