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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.
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“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]
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“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]
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“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]
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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
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