{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T12:53:32Z","timestamp":1793105612389,"version":"4.5.0"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003196","name":"Ministry of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003196","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003407","name":"Ministero dell\u2019Istruzione, dell\u2019Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","award":["118"],"award-info":[{"award-number":["118"]}],"id":[{"id":"10.13039\/501100003407","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003407","name":"Ministero dell\u2019Istruzione, dell\u2019Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","award":["2333"],"award-info":[{"award-number":["2333"]}],"id":[{"id":"10.13039\/501100003407","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021856","name":"Ministero dell'Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","award":["D93C23000450005"],"award-info":[{"award-number":["D93C23000450005"]}],"id":[{"id":"10.13039\/501100021856","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1016\/j.compmedimag.2025.102658","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T01:41:25Z","timestamp":1761183685000},"page":"102658","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"special_numbering":"C","title":["Multistain multicompartment automatic segmentation in renal biopsies with thrombotic microangiopathies and other vasculopathies"],"prefix":"10.1016","volume":"126","author":[{"given":"Nicola","family":"Altini","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michela","family":"Prunella","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Surya V.","family":"Seshan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Savino","family":"Sciascia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonella","family":"Barreca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Del Gobbo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Porubsky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hien Van","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudia","family":"Delprete","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Berardino","family":"Prencipe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dej\u00e1n","family":"Dobi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daan P.C.","family":"van Doorn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sjoerd A.M.E.G.","family":"Timmermans","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pieter","family":"van Paassen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3088-0788","authenticated-orcid":false,"given":"Vitoantonio","family":"Bevilacqua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan Ulrich","family":"Becker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compmedimag.2025.102658_bib1","doi-asserted-by":"crossref","first-page":"11590","DOI":"10.3389\/ti.2023.11590","article-title":"Thrombotic microangiopathy in the renal allograft: results of the TMA banff working group consensus on pathologic diagnostic criteria","volume":"36","author":"Afrouzian","year":"2023","journal-title":"Transpl. Int J. Eur. Soc. Organ Transpl."},{"issue":"3","key":"10.1016\/j.compmedimag.2025.102658_bib2","doi-asserted-by":"crossref","first-page":"503","DOI":"10.3390\/electronics9030503","article-title":"Semantic segmentation framework for glomeruli detection and classification in kidney histological sections","volume":"9","author":"Altini","year":"2020","journal-title":"Electronics"},{"issue":"11","key":"10.1016\/j.compmedimag.2025.102658_bib3","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.3390\/electronics9111768","article-title":"A deep learning instance segmentation approach for global glomerulosclerosis assessment in donor kidney biopsies","volume":"9","author":"Altini","year":"2020","journal-title":"Electronics"},{"key":"10.1016\/j.compmedimag.2025.102658_bib4","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2023.107814","article-title":"Performance and limitations of a supervised deep learning approach for the histopathological Oxford classification of glomeruli with IgA nephropathy","volume":"242","author":"Altini","year":"2023","journal-title":"Comput. Methods Prog. Biomed."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102658_bib5","doi-asserted-by":"crossref","first-page":"16878","DOI":"10.1038\/s41598-017-17204-5","article-title":"QuPath: open source software for digital pathology image analysis","volume":"7","author":"Bankhead","year":"2017","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102658_bib6","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.kint.2020.02.027","article-title":"Artificial intelligence and machine learning in nephropathology","volume":"98","author":"Becker","year":"2020","journal-title":"Kidney Int"},{"key":"10.1016\/j.compmedimag.2025.102658_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2025.102526","article-title":"A multimodal framework for assessing the link between pathomics, transcriptomics, and pancreatic cancer mutations","volume":"123","author":"Berloco","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102658_bib8","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1681\/ASN.2020050597","article-title":"Deep Learning\u2013Based segmentation and quantification in experimental kidney histopathology. J am soc nephrol","volume":"32","author":"Bouteldja","year":"2021","journal-title":"JASN"},{"key":"10.1016\/j.compmedimag.2025.102658_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2019.105273","article-title":"Glomerulosclerosis identification in whole slide images using semantic segmentation","volume":"184","author":"Bueno","year":"2020","journal-title":"Comput. Methods Prog. Biomed."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102658_bib10","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s40620-023-01775-w","article-title":"Time for a full digital approach in nephropathology: a systematic review of current artificial intelligence applications and future directions","volume":"37","author":"Cazzaniga","year":"2023","journal-title":"J. Nephrol."},{"key":"10.1016\/j.compmedimag.2025.102658_bib11","doi-asserted-by":"crossref","unstructured":"Chen L.C., Zhu Y., Papandreou G., Schroff F., Adam H., 2018. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In Proceedings of the European conference on computer vision (ECCV) (pp. 801-818).","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"10.1016\/j.compmedimag.2025.102658_bib12","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"1290","article-title":"Masked-attention Mask Transformer for Universal Image Segmentation","author":"Cheng","year":"2022"},{"key":"10.1016\/j.compmedimag.2025.102658_bib13","unstructured":"Geng Z., Guo M.H., Chen H., Li X., Wei K., Lin Z., 2021. Is Attention Better Than Matrix Decomposition?. arXiv preprint arXiv:2109.04553."},{"issue":"10","key":"10.1016\/j.compmedimag.2025.102658_bib14","doi-asserted-by":"crossref","first-page":"1953","DOI":"10.1681\/ASN.2018121259","article-title":"Computational segmentation and classification of diabetic glomerulosclerosis","volume":"30","author":"Ginley","year":"2019","journal-title":"J. Am. Soc. Nephrol."},{"key":"10.1016\/j.compmedimag.2025.102658_bib15","first-page":"1140","article-title":"SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation","volume":"35","author":"Guo","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"5","key":"10.1016\/j.compmedimag.2025.102658_bib16","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1016\/j.kint.2020.08.006","article-title":"Consensus definitions for glomerular lesions by light and electron microscopy: recommendations from a working group of the renal pathology society","volume":"98","author":"Haas","year":"2020","journal-title":"Kidney Int"},{"key":"10.1016\/j.compmedimag.2025.102658_bib17","doi-asserted-by":"crossref","unstructured":"He K., Zhang X., Ren S., Sun J., 2016. Deep Residual Learning for Image Recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102658_bib18","doi-asserted-by":"crossref","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":"10.1016\/j.compmedimag.2025.102658_bib19","series-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2261","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"issue":"7","key":"10.1016\/j.compmedimag.2025.102658_bib20","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1093\/ndt\/gfad039","article-title":"Deep learning automation of MEST-C classification in IgA nephropathy","volume":"38","author":"Jaugey","year":"2023","journal-title":"Nephrol. Dial. Transpl."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102658_bib21","doi-asserted-by":"crossref","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"},{"issue":"8","key":"10.1016\/j.compmedimag.2025.102658_bib22","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1016\/j.ajpath.2021.05.004","article-title":"A deep Learning-Based approach for glomeruli instance segmentation from multistained renal biopsy pathologic images","volume":"191","author":"Jiang","year":"2021","journal-title":"Am. J. Pathol."},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102658_bib23","doi-asserted-by":"crossref","first-page":"e18","DOI":"10.1016\/S2589-7500(21)00211-9","article-title":"Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study","volume":"4","author":"Kers","year":"2022","journal-title":"Lancet Digit Health"},{"key":"10.1016\/j.compmedimag.2025.102658_bib24","doi-asserted-by":"crossref","unstructured":"Lin T.Y., Doll\u00e1r P., Girshick R., He K., Hariharan B., Belongie S., 2017. Feature Pyramid Networks for Object Detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117-2125).","DOI":"10.1109\/CVPR.2017.106"},{"key":"10.1016\/j.compmedimag.2025.102658_bib25","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"12009","article-title":"Swin Transformer V2: Scaling Up Capacity and Resolution","author":"Liu","year":"2022"},{"key":"10.1016\/j.compmedimag.2025.102658_bib26","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"10012","article-title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.compmedimag.2025.102658_bib27","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"11976","article-title":"A ConvNet for the 2020s","author":"Liu","year":"2022"},{"issue":"1","key":"10.1016\/j.compmedimag.2025.102658_bib28","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1038\/s43856-022-00138-z","article-title":"A user-friendly tool for cloud-based whole slide image segmentation with examples from renal histopathology","volume":"2","author":"Lutnick","year":"2022","journal-title":"Commun. Med"},{"issue":"6","key":"10.1016\/j.compmedimag.2025.102658_bib29","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1111\/his.13716","article-title":"Characteristic morphological changes in anti-VEGF therapy-induced glomerular microangiopathy","volume":"73","author":"Pfister","year":"2018","journal-title":"Histopathology"},{"key":"10.1016\/j.compmedimag.2025.102658_bib30","series-title":"International Conference on Medical image computing and computer-assisted intervention","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","author":"Ronneberger","year":"2015"},{"issue":"11","key":"10.1016\/j.compmedimag.2025.102658_bib31","doi-asserted-by":"crossref","first-page":"1795","DOI":"10.1097\/TP.0000000000002366","article-title":"Clahsen-van Groningen M, haas M, henriksen KJ, horsfield C, et al. A 2018 reference guide to the banff classification of renal allograft pathology","volume":"102","author":"Roufosse","year":"2018","journal-title":"Transplantation"},{"key":"10.1016\/j.compmedimag.2025.102658_bib32","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2021.101930","article-title":"Automated assessment of glomerulosclerosis and tubular atrophy using deep learning","volume":"90","author":"Salvi","year":"2021","journal-title":"Comput. Med Imaging Graph"},{"key":"10.1016\/j.compmedimag.2025.102658_bib33","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2022.102104","article-title":"Boundary-aware glomerulus segmentation: toward one-to-many stain generalization","volume":"100","author":"Silva","year":"2022","journal-title":"Comput. Med Imaging Graph"},{"key":"10.1016\/j.compmedimag.2025.102658_bib34","series-title":"Proceedings of the 36th International Conference on Machine Learning [Internet]","first-page":"6105","article-title":"EfficientNet: rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019"},{"key":"10.1016\/j.compmedimag.2025.102658_bib35","series-title":"Proceedings of the 38th International Conference on Machine Learning [Internet]","first-page":"10096","article-title":"EfficientNetV2: smaller models and faster training","author":"Tan","year":"2021"},{"key":"10.1016\/j.compmedimag.2025.102658_bib36","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.neucom.2021.07.005","article-title":"Towards Histopathological Stain Invariance by Unsupervised Domain Augmentation using Generative Adversarial Networks","volume":"460","author":"Vasiljevi\u0107","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compmedimag.2025.102658_bib37","doi-asserted-by":"crossref","first-page":"110780","DOI":"10.1016\/j.knosys.2023.110780","article-title":"HistoStarGAN: A unified approach to stain normalisation, stain transfer and stain invariant segmentation in renal histopathology","volume":"277","author":"Vasiljevi\u0107","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.compmedimag.2025.102658_bib38","first-page":"12077","article-title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"3","key":"10.1016\/j.compmedimag.2025.102658_bib39","doi-asserted-by":"crossref","first-page":"441","DOI":"10.34067\/KID.0000000668","article-title":"Permutation-Invariant cascaded attentional set operator for computational nephropathology","volume":"6","author":"Zare","year":"2025","journal-title":"Kidney360"}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611125001673?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611125001673?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:46:19Z","timestamp":1773276379000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611125001673"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":39,"alternative-id":["S0895611125001673"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2025.102658","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multistain multicompartment automatic segmentation in renal biopsies with thrombotic microangiopathies and other vasculopathies","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2025.102658","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"102658"}}