The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Deep Learning-Based Histopathologic Assessment of Kidney Tissue
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In a recent study, Hermsen et al., successfully implemented a deep learning algorithm to divide kidney biopsies from different centers into their anatomical components (46).”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [46]
In the citing paper
“Most importantly, the authors proved that the same algorithm worked on histological samples from different centers, thereby addressing the issue of reproducibility.”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [46]
In the citing paper
“While the algorithm performed well in identifying healthy glomeruli, it struggled to identify more challenging structures such as sclerotic glomeruli or atrophic tubuli.”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [46]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
The Banff 97 working classification of renal allograft pathology
Racusen, L. C., Solez, K., Colvin, R. B., Bonsib, S. M., de Castro, M. C. R., Cavallo, T., Croker, B. P., Demetris, A. J., Drachenberg, C. B., Fogo, A. B., et al. (1999) · Kidney International · doi:10.1046/j.1523-1755.1999.00299.x · Cited by 3,026
Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen - van de Kaa, C., Bult, P., van Ginneken, B., & van der Laak, J. (2016) · Scientific Reports · doi:10.1038/srep26286 · Cited by 1,069
Self-citationA 2018 Reference Guide to the Banff Classification of Renal Allograft Pathology
Roufosse, C., Simmonds, N., Clahsen-van Groningen, M., Haas, M., Henriksen, K. J., Horsfield, C., Loupy, A., Mengel, M., Perkowska-Ptasińska, A., Rabant, M., et al. (2018) · Transplantation · doi:10.1097/tp.0000000000002366 · Cited by 751
Loupy, A., Haas, M., Solez, K., Racusen, L. C., Glotz, D., Serón, D., Nankivell, B. J., Colvin, R. B., Afrouzian, M., Akalin, E., et al. (2016) · American Journal of Transplantation · doi:10.1111/ajt.14107 · Cited by 629
The Substantial Loss of Nephrons in Healthy Human Kidneys with Aging
Đenić, A., Lieske, J. C., Chakkera, H. A., Poggio, E. D., Alexander, M. P., Singh, P. Y., Kremers, W. K., Lerman, L. O., & Rule, A. D. (2016) · Journal of the American Society of Nephrology · doi:10.1681/asn.2016020154 · Cited by 453
Tellez, D., Balkenhol, M., Otte-Höller, I., van de Loo, R., Vogels, R., Bult, P., Wauters, C., Vreuls, W., Mol, S., Karssemeijer, N., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2820199 · Cited by 288
Grimm, P. C., Nickerson, P. W., Gough, J., McKenna, R. M., Stern, E., Jeffery, J., & Rush, D. N. (2003) · Journal of the American Society of Nephrology · doi:10.1097/01.asn.0000066143.02832.5e · Cited by 193
Bukowy, J. D., Dayton, A., Cloutier, D., Manis, A. D., Staruschenko, A., Lombard, J. H., Woods, L. C. S., Beard, D. A., & Cowley, A. W. (2018) · Journal of the American Society of Nephrology · doi:10.1681/asn.2017111210 · Cited by 113
Digital Pathology Evaluation in the Multicenter Nephrotic Syndrome Study Network (NEPTUNE)
Barisoni, L. M. C., Nast, C. C., Jennette, J. C., Hodgin, J. B., Herzenberg, A. M., Lemley, K. V., Conway, C. M., Kopp, J. B., Kretzler, M., Lienczewski, C., et al. (2013) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.08370812 · Cited by 104
CNN cascades for segmenting sparse objects in gigapixel whole slide images
Gadermayr, M., Dombrowski, A.-K., Klinkhammer, B. M., Boor, P., & Merhof, D. (2018) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2018.11.002 · Cited by 101
Segmental HOG: new descriptor for glomerulus detection in kidney microscopy image
Kato, T., Relator, R., Ngouv, H., Hirohashi, Y., Takaki, O., Kakimoto, T., & Okada, K. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0739-1 · Cited by 94
CNN cascades for segmenting sparse objects in gigapixel whole slide images
Gadermayr, M., Dombrowski, A.-K., Klinkhammer, B. M., Boor, P., & Merhof, D. (2019) · RWTH Publications (RWTH Aachen) · Cited by 62
Glomerulus Classification with Convolutional Neural Networks
Pedraza, A., Gallego, J., López, S. G., Gonzalez, L., Laurinavičius, A., & Bueno, G. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-60964-5_73 · Cited by 60
Servais, A., Meas‐Yedid, V., Noël, L., Martinez, F., Panterne, C., Kreis, H., Zuber, J., Timsit, M., Legendre, C., Olivo‐Marín, J., et al. (2011) · American Journal of Transplantation · doi:10.1111/j.1600-6143.2011.03594.x · Cited by 47
On the concept of objectivity in digital image analysis in pathology
Tadrous, P. J. (2010) · Pathology · doi:10.3109/00313021003641758 · Cited by 44
Klapczynski, M., Gagné, G. D., Morgan, S. J., Larson, K. J., LeRoy, B. E., Blomme, E. A. G., Cox, B. F., & Shek, E. W. (2012) · Journal of Pathology Informatics · doi:10.4103/2153-3539.95456 · Cited by 34
The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images
Mitry, D., Zutis, K., Dhillon, B., Pető, T., Hayat, S. A., Khaw, K., Morgan, J. E., Moncur, W., Trucco, E., & Foster, P. J. (2016) · Translational Vision Science & Technology · doi:10.1167/tvst.5.5.6 · Cited by 32
Ginley, B. G., Tomaszewski, J. E., Yacoub, R., Chen, F., & Sarder, P. (2017) · Journal of medical imaging · doi:10.1117/1.jmi.4.2.021102 · Cited by 31
Gadermayr, M., Klinkhammer, B. M., Boor, P., & Merhof, D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47157-0_3 · Cited by 15
Computational analysis of the structural progression of human glomeruli in diabetic nephropathy
Sarder, P., Ginley, B. G., Tomaszewski, J. E., Jen, K., Fogo, A. B., & Jain, S. R. (2018) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2295249 · Cited by 8
Flagged first, then by citations
Cited by
Sorted by citations
Deep learning in histopathology: the path to the clinic
van der Laak, J., Litjens, G., & Ciompi, F. (2021) · Nature Medicine · doi:10.1038/s41591-021-01343-4 · Cited by 902
“An example of a CPATH application for automatic tissue segmentation using a combination of U-Net models 20 , as well as the corresponding ground truth, is shown in Fig.”
BackgroundMethodLoupy, A., Haas, M., Roufosse, C., Naesens, M., Adam, B., Afrouzian, M., Akalin, E., Alachkar, N., Bagnasco, S., Becker, J. U., et al. (2020) · American Journal of Transplantation · doi:10.1111/ajt.15898 · Cited by 834
Deep learning in cancer pathology: a new generation of clinical biomarkers
Echle, A., Rindtorff, N. T., Brinker, T. J., Luedde, T., Pearson, A. T., & Kather, J. N. (2021) · British Journal of Cancer · doi:10.1038/s41416-020-01122-x · Cited by 667
Fibrosis in Chronic Kidney Disease: Pathogenesis and Consequences
Panizo, S., Martínez‐Arias, L., Alonso‐Montes, C., Cannata, P., Martín-Carro, B., Martín, J. L. F., Naves‐Díaz, M., Carrillo‐López, N., & Cannata-Andía, J. B. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22010408 · Cited by 402
Digital pathology and computational image analysis in nephropathology
Barisoni, L., Lafata, K. J., Hewitt, S. M., Madabhushi, A., & Balis, U. G. J. (2020) · Nature Reviews Nephrology · doi:10.1038/s41581-020-0321-6 · Cited by 264
Metadata
- 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
- Venue
- Journal of the American Society of Nephrology
- Published
- 2019-09-05
- Volume
- 30
- Issue
- 10
- Pages
- 1968-1979
- ISSN
- 1046-6673 (ISSN-L)
- 1533-3450
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
