{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T10:50:33Z","timestamp":1780051833368,"version":"3.53.1"},"reference-count":44,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T00:00:00Z","timestamp":1696982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Social Fund","award":["09.3.3-LMT-K-712-19-0186"],"award-info":[{"award-number":["09.3.3-LMT-K-712-19-0186"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Introduction The diagnosis of glomerular diseases is primarily based on visual assessment of histologic patterns. Semi-quantitative scoring of active and chronic lesions is often required to assess individual characteristics of the disease. Reproducibility of the visual scoring systems remains debatable, while digital and machine-learning technologies present opportunities to detect, classify and quantify glomerular lesions, also considering their inter- and intraglomerular heterogeneity. Materials and methods: We performed a cross-validated comparison of three modifications of a convolutional neural network (CNN)-based approach for recognition and intraglomerular quantification of nine main glomerular patterns of injury. Reference values provided by two nephropathologists were used for validation. For each glomerular image, visual attention heatmaps were generated with a probability of class attribution for further intraglomerular quantification. The quality of classifier-produced heatmaps was evaluated by intersection over union metrics (IoU) between predicted and ground truth localization heatmaps. Results: A proposed spatially guided modification of the CNN classifier achieved the highest glomerular pattern classification accuracies, with area under curve (AUC) values up to 0.981. With regards to heatmap overlap area and intraglomerular pattern quantification, the spatially guided classifier achieved a significantly higher generalized mean IoU value compared to single-multiclass and multiple-binary classifiers. Conclusions: We propose a spatially guided CNN classifier that in our experiments reveals the potential to achieve high accuracy for the localization of intraglomerular patterns.<\/jats:p>","DOI":"10.3390\/jimaging9100220","type":"journal-article","created":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T03:07:47Z","timestamp":1697080067000},"page":"220","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Spatially Guided Machine-Learning Method to Classify and Quantify Glomerular Patterns of Injury in Histology Images"],"prefix":"10.3390","volume":"9","author":[{"given":"Justinas","family":"Besusparis","sequence":"first","affiliation":[{"name":"Faculty of Medicine, Vilnius University, M.K.Ciurlionio 21, LT-03101 Vilnius, Lithuania"},{"name":"National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, P. Baublio 5, LT-08406 Vilnius, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1360-6533","authenticated-orcid":false,"given":"Mindaugas","family":"Morkunas","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Vilnius University, M.K.Ciurlionio 21, LT-03101 Vilnius, Lithuania"},{"name":"National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, P. Baublio 5, LT-08406 Vilnius, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arvydas","family":"Laurinavicius","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Vilnius University, M.K.Ciurlionio 21, LT-03101 Vilnius, Lithuania"},{"name":"National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, P. Baublio 5, LT-08406 Vilnius, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1111\/j.1523-1755.2004.00443.x","article-title":"The classification of glomerulonephritis in systemic lupus erythematosus revisited","volume":"65","author":"Weening","year":"2004","journal-title":"Kidney Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1681\/ASN.2010010010","article-title":"Pathologic classification of diabetic nephropathy","volume":"21","author":"Tervaert","year":"2010","journal-title":"J. Am. Soc. Nephrol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1053\/j.ajkd.2003.10.024","article-title":"Pathologic classification of focal segmental glomerulosclerosis: A working proposal","volume":"43","author":"Fogo","year":"2004","journal-title":"Am. J. 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