Cited by 344AI in cancer detectionMedical Image Segmentation TechniquesRadiomics and Machine Learning in Medical Imaging

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Deep Learning-Based Histopathologic Assessment of Kidney Tissue

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Hermsen, Meyke, de Bel, Thomas, den Boer, Marjolijn, Steenbergen, Eric J., Kers, Jesper, Florquin, Sandrine, Roelofs, Joris J. T. H., Stegall, Mark D., Alexander, Mariam P., Smith, Byron H., Smeets, Bart, Hilbrands, Luuk B., van der Laak, Jeroen A. W. M.·2019·Journal of the American Society of Nephrology

DOI 10.1681/asn.2019020144PMC PMC6779356

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Abstract

Significance Statement Histopathologic assessment of kidney tissue currently relies on manual scoring or traditional image-processing techniques to quantify and classify tissue features, time-consuming approaches that have limited reproducibility. The authors present an alternative approach, featuring a convolutional neural network for multiclass segmentation of kidney tissue in sections stained by periodic acid–Schiff. Their findings demonstrate applicability of convolutional neural networks for tissue from multiple centers, for biopsies and nephrectomy samples, and for the analysis of both healthy and pathologic tissues. In addition, they validated the network’s results with components from the Banff classification system. Their convolutional neural network may have utility for quantitative studies involving kidney histopathology across centers and potential for application in routine diagnostics. Background The development of deep neural networks is facilitating more advanced digital analysis of histopathologic images. We trained a convolutional neural network for multiclass segmentation of digitized kidney tissue sections stained with periodic acid–Schiff (PAS). Methods We trained the network using multiclass annotations from 40 whole-slide images of stained kidney transplant biopsies and applied it to four independent data sets. We assessed multiclass segmentation performance by calculating Dice coefficients for ten tissue classes on ten transplant biopsies from the Radboud University Medical Center in Nijmegen, The Netherlands, and on ten transplant biopsies from an external center for validation. We also fully segmented 15 nephrectomy samples and calculated the network’s glomerular detection rates and compared network-based measures with visually scored histologic components (Banff classification) in 82 kidney transplant biopsies. Results The weighted mean Dice coefficients of all classes were 0.80 and 0.84 in ten kidney transplant biopsies from the Radboud center and the external center, respectively. The best segmented class was “glomeruli” in both data sets (Dice coefficients, 0.95 and 0.94, respectively), followed by “tubuli combined” and “interstitium.” The network detected 92.7% of all glomeruli in nephrectomy samples, with 10.4% false positives. In whole transplant biopsies, the mean intraclass correlation coefficient for glomerular counting performed by pathologists versus the network was 0.94. We found significant correlations between visually scored histologic components and network-based measures. Conclusions This study presents the first convolutional neural network for multiclass segmentation of PAS-stained nephrectomy samples and transplant biopsies. Our network may have utility for quantitative studies involving kidney histopathology across centers and provide opportunities for deep learning applications in routine diagnostics.

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Background
Development of a multiple convolutional neural network–facilitated diagnostic screening program for immunofluorescence images of IgA nephropathy and idiopathic membranous nephropathy
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Background
Ginsenoside Rg1 alleviates renal inflammation and fibrosis through regulating Nrf2-TRPC6 signalling pathway in mice
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Method
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Background
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Result
Artificial intelligence in nephrology
Anand, S. K., & Verma, A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15688-5.00035-8 · Cited by 3
Künstliche Intelligenz in der Nierentransplantationspathologie
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“…that there is considerable subjectivity in the assessment of transplant biopsies by pathologists (for example, counting of glomeruli) that could be eliminated by using a CNN-based model and it suggested that these algorithms should be further explored in the diagnosis of transplant biopsies [20].”
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Li, F., Yang, N., Hou, X., Xie, C., Wang, J., Lv, C., & Xie, G. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9176234 · Cited by 3
“Recent works in renal pathology images were mainly concentrate in the localization of glomeruli[7-9], tubulin[10], and preliminary classification task[11].”
Background
Analysis of the Diagnosis Model of Peripheral Non-Small-Cell Lung Cancer under Computed Tomography Images
Xie, Z., & Zhang, H. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/3107965 · Cited by 3
“,e larger the Dice coefficient value, the higher the similarity between the proposed method’s segmentation effect and the gold standard [16, 17].”
Background
Retracted
7-Amino-4-methylcoumarin as a fluorescent substitute for Schiff’s reagent: a new method that can be combined with hemalum and eosin staining on the same tissue section
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“For pathologic diagnosis, PAS staining provides useful and sometimes critical information about the lesions under investigation, which often leads to accurate diagnosis (Agarwal et al. 2013; Forţofoiu et al. 2017; Roden and Schuetz 2017; Hermsen et al. 2019; Kerckhoffs et al. 2020).”
BackgroundMethod
Machine learning in medicine: Medical droids, tricorders, and a computer named Hal 9000
Bajema, I. M. (2021) · Néphrologie & Thérapeutique · doi:10.1016/j.nephro.2020.03.002 · Cited by 3
Cellular and molecular interrogation of kidney biopsy specimens
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Semi-Supervised Segmentation of Renal Pathology: An Alternative to Manual Segmentation and Input to Deep Learning Training
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“While RCNN models entail high training time require many samples, they are most generalizable therafter and have been and have been used in kidney pathologic segmentation [7],[8].”
Method
Deep Learning Using Electrocardiograms in Patients on Maintenance Dialysis
Zheng, Z., Soomro, Q. H., & Charytan, D. M. (2023) · Advances in Kidney Disease and Health · doi:10.1053/j.akdh.2022.11.009 · Cited by 5
Spatial Transcriptomics Inferred from Pathology Whole-Slide Images Links Tumor Heterogeneity to Survival in Breast and Lung CancerPreprint
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Automated Quantification of Chronic Changes in the Kidney Biopsy: Another Step in the Right Direction
Hodgin, J. B., & Mariani, L. H. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2021020240 · Cited by 3
Machine Learning in Education
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Development of a deep learning-based histological evaluation model for critical-size bone defect healing in rats – an objective tool
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The Berberine Derivative BBR684 Inhibits VDAC Oligomerization to Suppress Ferroptosis in Acute Kidney Injury
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“27 , 44 Compared to the FA group, BBR684-treated group exhibited signiﬁcantly lower scores for renal tubular dilation and brush border injury, indicating effective attenuation of FA-induced tubular damage ( Figure 6 A and F).”
Applications of artificial intelligence algorithms in ultrasound-based kidney stone detection, classification, prediction, and management: a systematic review
Elhaie, M., Koozari, A., Albeshan, S. M., & Mozafari, M. H. (2026) · Abdominal Radiology · doi:10.1007/s00261-026-05419-y · Cited by 2
“DL models, such as convolutional neu - ral networks (CNNs), have been applied to US images for kidney stone detection and image-level classification(Stone or non-Stone), demonstrating promising results in identifying stone presence, size, and image-level classification with high accuracy [14–18].”
Leveraging ICT Tools to Improve Kidney Health: A Comprehensive Review of Innovations in Nephrology
Mata-Lima, A., Olmedo, J. J. S., & Paquete, A. R. (2026) · Healthcare · doi:10.3390/healthcare14060785 · Cited by 2
“Evidence Evidence is heterogeneous, long-term outcome data are limited for some technologies, and challenges related to data privacy, regulatory oversight, reimbursement and digital equity persist [17,18].”
Digital pathology all stars
Eccher, A., Girolami, I., & Scarpa, A. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100125 · Cited by 2
“Our previous research has shown how AI can successfully delineate (‘image segmentation’) anatomical structures (possibly displaying pathological changes) in renal biopsies.(16) Also, we developed techniques to automatically detect immunohistochemically stained lymphocytes.”
Method
Glomerulosclerosis detection with pre-trained CNNs ensemble
Santos, J. D., Silva, R., de Oliveira, L. R., dos‐Santos, W. L. C., Aldeman, N. L. S., Duarte, A. A., & Veras, R. D. M. S. (2022) · Computational Statistics · doi:10.1007/s00180-022-01307-3 · Cited by 2
“Hermsen et al. (2019) presented an approach featuring a CNN for multiclass segmentation of kidney tissue in sections stained by periodic acid–Schiff.”
Multi-Object Segmentation of Coronary Artery OCT Images Based on Multi-Scale Deeping Learning Method
Cheng, H., Ren, S., & Zhang, W. (2023) · Chinese Control Conference (CCC) · doi:10.23919/ccc58697.2023.10240701 · Cited by 2
Computational approaches for hematopoietic stem cells
Raghav, P. K., Banerjee, B., Kumar, R., Raghav, A., Lathwal, A., & Chadha, R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13222-3.00013-7 · Cited by 2
Mlp4green: A Binary Classification Approach Specifically for Green Odor
Yang, J., Qian, Z., He, Y., Liu, M., Li, W., & Han, W. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25063515 · Cited by 2
Comparative analysis of chronic progressive nephropathy (CPN) diagnosis in rat kidneys using an artificial intelligence deep learning model
Bae, Y., Byun, J., Lee, H., & Han, B. (2024) · Toxicological Research · doi:10.1007/s43188-024-00247-y · Cited by 3
“These deep-learning models have also been used to analyze slides of normal organs from non-clinical tests [26, 27] and naturally occurring lesions or lesions caused by the administration of test substances [6, 28–30].”
Toxicological and histopathological effects of sodium benzoate used in commercially available fruit juice on liver and kidney tissue in mice model
Akter, D., Islam, M. M., Hossain, M. I., Esrafil, M., Dey, B. P., Bari, L., & Zubair, M. A. (2024) · Clinical Nutrition Open Science · doi:10.1016/j.nutos.2024.10.015 · Cited by 2
Will AI Replace Physicians? A Review of Artificial Intelligence Applications in Kidney Transplantation
Lai, W., Min, J., & Peng, L. (2025) · Organ medicine. · doi:10.1002/orm2.70001 · Cited by 2
Deep learning model for detecting high-grade dysplasia in colorectal adenomas
Steimetz, E., SIMSEK, Z. C., Saha, A., Xia, R., & Gupta, R. (2025) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2025.100441 · Cited by 3
Nonlinear Imaging Detection of Organ Fibrosis in Minute Samples for Early Stage Utilizing Dual-Channel Two-Photon and Second-Harmonic Excitation
Yu, B., Cheng, Q.-D., Liu, Y., Zhang, R., Li, D., Wang, A., Feng, L., & Jia, X. D. (2025) · Biosensors · doi:10.3390/bios15060357 · Cited by 2
The Microscope and Beyond: Current Trends in the Characterization of Kidney Allograft Rejection From Tissue Samples
Chauveau, B., Couzi, L., & Merville, P. (2024) · Transplantation · doi:10.1097/tp.0000000000005153 · Cited by 2
Quantitative assessment of renal obstruction in multi-phase CTU using automatic 3D segmentation of the renal parenchyma and renal pelvis: A proof of concept
Kahn, C., Leichter, I. S., Lederman, R., Sosna, J., Duvdevani, M., & Yeshua, T. (2022) · European Journal of Radiology Open · doi:10.1016/j.ejro.2022.100458 · Cited by 2
Glomerulus segmentation in kidney histopathology images: A UNET-based approach with MobileNetV2 encoder
Kaur, G., Garg, M., & Gupta, S. (2024) · AIP conference proceedings · doi:10.1063/5.0228066 · Cited by 1
nPOD-Kidney: A Heterogenous Donor Cohort for the Investigation of Diabetic Kidney Disease Pathogenesis and Progression
Ward, H. H., Anquetil, F., Das, V., Gibson, C. B., Dovmark, T. H., Kusmartseva, I., Yang, M., Beery, M. L., Atkinson, M. A., Zeng, X., et al. (2024) · Kidney360 · doi:10.34067/kid.0000000620 · Cited by 1
“45,46 Thus, the nPOD-K cohort reproduced major DKD features, and the computational work ﬂ ows developed could assist researchers in unbiased spatial evaluation.”
MIE: Magnification-integrated ensemble method for improving glomeruli segmentation in medical imaging
Han, Y., Kim, J., Park, S., Moon, J.-S., & Lee, J.-H. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109041 · Cited by 1
Using Predictive Models and AI for AKI Research
Rajagopal, M., Chan, L., & Nadkarni, G. N. (2025) · Seminars in Nephrology · doi:10.1016/j.semnephrol.2025.151673 · Cited by 1
Non-diabetic Kidney Disease in Type 2 Diabetes: From Kidney Biopsy to Precision Medicine
Sreedharan, S., Mohandas, M. K., Vismaya, K. B., & Raju, N. (2026) · Cureus · doi:10.7759/cureus.100716 · Cited by 2
“Despite these promising results, clinical implementation of proteomic classifiers has been limited by several challenges, including the need for specialized equipment, standardization issues, and cost considerations [65,66].”
Zebra bodies recognition by artificial intelligence (ZEBRA): a computational tool for Fabry nephropathy
Cazzaniga, G., Carbone, M., Barretta, R., Casati, G., Vatrano, S., Gambaro, G., Vischini, G., Capelli, I., Mignani, R., Pasquinelli, G., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-35466-w · Cited by 1
“AI applications have been explored for tasks such as glomerular classification, segmentation, and quantification of histological features, aiming to augment traditional pathology workflow 25 .”
Glomeruli detection and classification in histopathological images using deep learning semantic segmentation
Juan-Ferrer, P., Pérez-Sánchez, N., Pla, F., Mollineda, R. A., Roselló-Sastre, E., & Tajahuerce, E. (2026) · BMC Medical Imaging · doi:10.1186/s12880-026-02178-6 · Cited by 1
“However, microscopic analysis of kidney biopsy slides is labor-intensive, subjective, and prone to inter-observer variability, usually leading to inconsistent diagnoses [4].”
Artificial Intelligence in Nephrology—State of the Art on Theoretical Background, Molecular Applications, and Clinical Interpretation
Stojanowski, J., Gołębiowski, T., & Musiał, K. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27031285 · Cited by 1
“Deep learning is used in analyzing radiological and histopathological images, including detecting differences in unsupervised learning or detecting specific structures or pathologies in supervised learning [37–39 ,43].”
Quantification of interstitial fibrosis in digitized kidney biopsies using deep neural networks
Soleimani, N., Hajizadeh, Z., Mohammadi, M., & Mohammadzadeh, S. (2026) · Diagnostic Pathology · doi:10.1186/s13000-026-01763-1 · Cited by 1
“So, IFTA assessment can suffer from suboptimal inter-pathologist reliability [16].”
Transforming chronic kidney disease care with telemedicine: expert consensus recommendations from the Italian Society of Nephrology
Lentini, P. L. M., Bianchi, S., Viglino, G., Pieruzzi, F., Grandaliano, G., Pesce, F., Stallone, G., & Gregorini, M. C. (2026) · Journal of Nephrology · doi:10.1093/joneph/aajaf007 · Cited by 1
Advances in Nephrology
Speeckaert, M. M. (2026) · Diagnostics · doi:10.3390/diagnostics16132117 · Cited by 1
Next-generation kidney tissue analysis — spatial omics and digital pathology
Yoshikawa, T., Bülow, R. D., Boor, P., & Yanagita, M. (2026) · Nature Reviews Nephrology · doi:10.1038/s41581-026-01102-z · Cited by 3
Glomerular Size in Transplanted Kidneys: Does Size Matter?
Nankivell, B. J. (2020) · Transplantation · doi:10.1097/tp.0000000000003571 · Cited by 1
Application of cloud server-based machine learning for assisting pathological structure recognition in IgA nephropathy
Huang, Y.-L., Liu, X. Q., Huang, Y., Jin, F. Y., Zhao, Q., Wu, Q. Y., & Ma, K. L. (2023) · Journal of Clinical Pathology · doi:10.1136/jcp-2023-209215 · Cited by 1
Fortschritte in der computergestützten quantitativen Nephropathologie
Bülow, R. D., Droste, P., & Boor, P. (2024) · Die Pathologie · doi:10.1007/s00292-024-01300-1 · Cited by 1
Advancing the application of the analytical renal pathology system in allograft IgA nephropathy patients
Liu, X., Fang, H., Liang, D., Lei, Q., Wang, J., Xu, F., Liang, S., Liang, D., Yang, F., Li, H., et al. (2024) · Renal Failure · doi:10.1080/0886022x.2024.2322043 · Cited by 2
“Notably, the application of convolutional neural networks (CNNs) has yielded promising outcomes, leading to their widespread utilization for auxiliary diagnosis via whole-slide image analysis [10–12].”
Background
SegRep: Mask-Supervised Learning for Segment Representation in Pathology Images
Yang, C., Komura, D., Ochi, M., Kakiuchi, M., Katoh, H., Ushiku, T., & Ishikawa, S. (2024) · IEEE Access · doi:10.1109/access.2024.3470213 · Cited by 1
Forging the tools for a computer-aided workflow in transplant pathology
Arthurs, C., & Roufosse, C. A. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00254-5 · Cited by 1
Kidney Diseases: Challenges and Opportunities of the Third Millenium. How can digital health help the National Health System?
Bellasi, A., Iorio, B. R. D., & Lullo, L. D. (2020) · AboutOpen · doi:10.33393/abtpn.2020.2029 · Cited by 1
Commentary: The Digital Fate of Glomeruli in Renal Biopsy
Girolami, I., Marletta, S., & Eccher, A. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_102_20 · Cited by 1
“…to renal biopsy mainly deal with quantification of rejection related inflammatory elements or interstitial fibrosis, [30‑32] studies focusing specifically on glomeruli have started to be published, both for transplant biopsies, [33,34] primary disease [35] and animal model of the disease.”
Background
Characterizing Tissue Structures from Spatial Omics with Spatial Cellular Graph PartitionPreprint
Wu, Z., Kondo, A., McGrady, M., Baker, E. A. G., Wu, E., Rahim, M. K., Bracey, N. A., Charu, V., Cho, R. J., Cheng, J. B., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.05.556133 · Cited by 1
“Similarly, recognizing tissue structures from unseen hematoxylin and eosin (H&E) stained pathology images is a widely-studied task often resolved in a supervised manner 33–36 .”
Background
Application of Multi-Scale Fusion Attention U-Net to Segment the Thyroid Gland on CT Localization Images for RadiotherapyPreprint
Wen, X., Zhao, B., Yuan, M., Li, J., Sun, M., Ma, L., Sun, C., & Yang, Y. (2021) · Research Square · doi:10.21203/rs.3.rs-949323/v1
“155 DSC(19)and JSC(20) calculation is shown in formulas (1.2) and (1,3): 156 , (1.2), 157 , (1.3), 158 where X represents the standard segmentation map drawn by a radiologist, Y is the prediction image 159 segmented by the neural network model, and |𝑋 ∩ 𝑌| represents the overlap between the standard map 160 drawn by t…”
BackgroundInfluential
Structural Knowledge Transfer of Panoptic Kidney Segmentation to Other Stains, Organs, and SpeciesPreprint
Ginley, B. G., Jen, K., & Sarder, P. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.21.465370
“2 To investigate the performance of panoptic segmentation for digital pathology segmentation tasks, we trained a panoptic feature pyramid network (pFPN) on a large dataset ( n = 126) of periodic acid-Schiff (PAS) stained human kidney biopsies, fully annotated for interstitium (group type object),…”
Method
ARTIFICIAL INTELLIGENCE TECHNOLOGIES FOR MORPHOLOGY: OPPORTUNITIES AND PROSPECTS
Иванова, Е., Grinin, V., Bakulina, A., & Timashev, P. (2021) · BIOTECHNOLOGY STATE OF THE ART AND PERSPECTIVES · doi:10.37747/10.37747/2312-640x-2021-19-155-158
Maurice Blanchot (Book)
Botting, F. (1997) · European Journal of English Studies
Biomedical Image Processing and Classification
Mesin, L. (2021) · MDPI eBooks · doi:10.3390/books978-3-0365-0347-9
A hybrid descriptor to improve kidney pathologies classification
da Silva, L. C., Machado, V. P., Veras, R. D. M. S., Aita, K. M. D. S. U., do Monte, S. J. H., & Aldeman, N. L. S. (2022) · ACM Symposium on Applied Computing · doi:10.1145/3477314.3507321
“[7] presented an approach featuring a convolutional neural network for multiclass segmentation of kidney tissue in sections stained by periodic acid–Schiff.”
Method
Clinicopathological features and short outcomes of oliguric acute tubular injury
Huang, M., Liu, J., Zhao, D., Rao, X., Ji, Y., Dong, Y., Meng, J., Chen, J., Zhang, L., Cao, X., et al. (2022) · Journal of Critical Care · doi:10.1016/j.jcrc.2022.154076
Automatische Bildanalyse und künstliche Intelligenz in der Nephropathologie
Bülow, R. D., Hölscher, D. L., & Boor, P. (2022) · Die Nephrologie · doi:10.1007/s11560-022-00598-3
Protocol biopsies after kidney transplantation
Keijbeck, A. A. (2023) · doi:10.26481/dis.20230214ak

Authors: Hermsen, M., de Bel, T., den Boer, M., Steenbergen, E. J., Kers, J., Florquin, S., Roelofs, J. J. T. H., Stegall, M. D., Alexander, M. P., Smith, B. H., Smeets, B., Hilbrands, L. B., & van der Laak, J. A. W. M.
Affiliations: Pathology and
Harvard University (US)
Amsterdam University Medical Centers (NL)
Mayo Clinic (US)
Radboud University Medical Center (NL)
ORCID: Hermsen, Meyke
Kers, Jesper
Florquin, Sandrine
Roelofs, Joris J. T. H.
Stegall, Mark D.
Alexander, Mariam P.
Smith, Byron H.
Smeets, Bart
Hilbrands, Luuk B.
Venue: Journal of the American Society of Nephrology
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2019-09-05
Volume: 30
Issue: 10
Pages: 1968-1979
ISSN: 1046-6673 (ISSN-L)
1533-3450
DOI: 10.1681/asn.2019020144
PMC: PMC6779356
OpenAlex: W2972214324
Cited by: 344
References: 23
Citations by year: 2026: 30 · 2025: 38 · 2024: 60 · 2023: 57 · 2022: 56 · 2021: 55 · 2020: 43 · 2019: 4
FWCI: 18.02
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Journal of the American Society of Nephrology (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Pure Amsterdam UMC (publishedVersion, other-oa)
Data Archiving and Networked Services (DANS) (submittedVersion)
Leiden Repository (Leiden University) (submittedVersion)
Amsterdam UMC (VU Amsterdam) - Institutional Repository (submittedVersion)
Radboud Repository (Radboud University) (submittedVersion)
Radboud Repository (Radboud University) (submittedVersion, other-oa)
Clinical Journal of the American Society of Nephrology
Funders: Horizon 2020 Framework Programme (9003035004)
Dutch Kidney Foundation (17OKG23)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords: kidney histopathology, convolutional neural networks, PAS staining, kidney transplant biopsy, Banff classification, whole slide images, quantitative histopathology, interstitial space
MeSH terms: Deep Learning, Biopsy, Humans, Kidney, Nephrectomy, Kidney Transplantation

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Deep Learning-Based Histopathologic Assessment of Kidney Tissue

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