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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Hermsen et al. developed and successfully implemented a deep learning algorithm (a convolutional neural network) that divided kidney biopsies from different centers into their anatomical components.
“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]
The same Hermsen et al. algorithm worked on histological samples from different centers, thereby addressing the issue of reproducibility.
“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]
The Hermsen et al. algorithm performed well in identifying healthy glomeruli but struggled to identify more challenging structures such as sclerotic glomeruli or atrophic tubuli.
“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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Girolami, I., Pantanowitz, L., Marletta, S., Hermsen, M., van der Laak, J. A. W. M., Munari, E., Furian, L., Vistoli, F., Zaza, G., Cardillo, M., et al. (2022) · Journal of Nephrology · doi:10.1007/s40620-022-01327-8 · Cited by 48 · see citing works
“In particular, Hermsen et al. [39] developed a multiclass segmentation CNN that achieved a high Dice coefficient for all of the segmentation classes (“glomeruli,” “sclerotic glomeruli,” “empty Bowman’s capsules,” “proxi-mal tubuli,” “distal tubuli,” “atrophic tubuli,” “undefined tubuli,” “capsule,”…”
Đenić, A., Elsherbiny, H. E., Mullan, A. F., Leibovich, B. C., Thompson, R. H., Ricaurte, L., Narasimhan, R., Kremers, W. K., Alexander, M. P., Lieske, J. C., et al. (2020) · Journal of the American Society of Nephrology · doi:10.1681/asn.2020040449 · Cited by 48 · see citing works
Deep-Learning–Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies
Zheng, Y., Cassol, C. A., Jung, S., Veerapaneni, D., Chitalia, V. C., Ren, K. Y. M., Bellur, S. S., Boor, P., Barisoni, L. M. C., Waikar, S. S., et al. (2021) · American Journal Of Pathology · doi:10.1016/j.ajpath.2021.05.005 · Cited by 49 · see citing works
Banff Digital Pathology Working Group: Going Digital in Transplant Pathology
Farris, A. B., Moghe, I., Wu, S., Hogan, J., Cornell, L. D., Alexander, M. P., Kers, J., Demetris, A. J., Levenson, R. M., Tomaszewski, J., et al. (2020) · American Journal of Transplantation · doi:10.1111/ajt.15850 · Cited by 48 · see citing works
Zhao, D., Wang, W., Tang, T., Zhang, Y., & Yu, C. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.05.029 · Cited by 47 · see citing works
“Examples of applications of AI in digital pathology include the detection and segmentation of kidney structures, the auxiliary diagnosis of renal pathological changes, and CKD prognosis [71–74].”
BackgroundDeep learning–based molecular morphometrics for kidney biopsies
Zimmermann, M., Klaus, M., Wong, M. N., Thebille, A.-K., Gernhold, L., Kuppe, C., Halder, M., Kranz, J., Wanner, N., Braun, F., et al. (2021) · JCI Insight · doi:10.1172/jci.insight.144779 · Cited by 47 · see citing works
“Generative networks have been used in the past for histopathological analysis, but mostly have been limited to classical histological stainings (28,29).”
MethodAssessment of glomerular morphological patterns by deep learning algorithms
Weis, C., Bindzus, J. N., Voigt, J., Runz, M., Hertjens, S., Gaida, M. M., Popović, Z. V., & Porubský, Š. (2022) · Journal of Nephrology · doi:10.1007/s40620-021-01221-9 · Cited by 45 · see citing works
“Algorithms for segmentation of healthy kidney parenchyma have been previously successfully developed [4, 5].”
MethodXie, G., Chen, T., Li, Y., Chen, T., Li, X., & Liu, Z. (2019) · Kidney Diseases · doi:10.1159/000504600 · Cited by 44 · see citing works
“Along with the work by Chen et al. [16], other endeavors have been made in developing AI for the prediction, diagnosis, and treatment of kidney diseases [18–23].”
Kidney Structural Features from Living Donors Predict Graft Failure in the Recipient
Issa, N. S., Lopez, C., Đenić, A., Taler, S. J., Larson, J. J., Kremers, W. K., Ricaurte, L., Merzkani, M. A., Alexander, M. P., Chakkera, H. A., et al. (2020) · Journal of the American Society of Nephrology · doi:10.1681/asn.2019090964 · Cited by 44 · see citing works
Developing image analysis pipelines of whole-slide images: Pre- and post-processing
Smith, B. H., Hermsen, M., Lesser, E. R., Ravichandar, D., & Kremers, W. K. (2020) · Journal of Clinical and Translational Science · doi:10.1017/cts.2020.531 · Cited by 43 · see citing works
“Proper control of these sources of error can result in robust deep learning models that can process data across scanners regardless of file format [15].”
BackgroundHermsen, M., Volk, V., Bräsen, J. H., Geijs, D. J., Gwinner, W., Kers, J., Linmans, J., Schaadt, N. S., Schmitz, J., Steenbergen, E. J., et al. (2021) · Laboratory Investigation · doi:10.1038/s41374-021-00601-w · Cited by 42 · see citing works
Omni-Seg: A Scale-Aware Dynamic Network for Renal Pathological Image Segmentation
Deng, R., Liu, Q., Cui, C., Yao, T., Long, J., Asad, Z., Womick, R. M., Zhu, Z., Fogo, A. B., Zhao, S., et al. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3260739 · Cited by 41 · see citing works
“Deep learning-based segmentation algorithms have shown the capability of performing multi-label medical image segmentation [13], [15], [28].”
El‐Achkar, T. M., Eadon, M. T., Kretzler, M., Himmelfarb, J., Lake, B. B., Zhang, K., Lecker, S. H., Morales, A., Bogen, S. A., Amodu, A., et al. (2023) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2023.08.015 · Cited by 39 · see citing works
Lee, J., Warner, E., Shaikhouni, S., Bitzer, M., Kretzler, M., Gipson, D. S., Pennathur, S., Bellovich, K. A., Bhat, Z. Y., Gadegbeku, C. A., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-08974-8 · Cited by 44 · see citing works
“Hermsen et al. used deep learning to segment several histologic structures on PAS stained tissue, including glomeruli, proximal and distal tubules, atrophic tubules and blood vessels 25 .”
BackgroundGallego, J., Świderska-Chadaj, Ż., Markiewicz, T., Yamashita, M., Gabaldon, M.-A., & Gertych, A. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101865 · Cited by 38 · see citing works
Zheng, Z., Zhang, X., Ding, J., Zhang, D., Cui, J., Fu, X., Han, J., & Zhu, P. (2021) · Diagnostics · doi:10.3390/diagnostics11111983 · Cited by 38 · see citing works
“There are many approaches for renal image segmentation to obtain the most detailed image quantification [18,24,35,36].”
MethodRicaurte, L., Đenić, A., Mullan, A. F., Narasimhan, R., Bogojevic, M., Thompson, R. H., Leibovich, B. C., Sangaralingham, S. J., Smith, M. L., Alexander, M. P., et al. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2021020267 · Cited by 38 · see citing works
Ho, D. J., Chui, M. H., Vanderbilt, C., Jung, J., Robson, M. E., Park, C.-S., Roh, J., & Fuchs, T. J. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100160 · Cited by 37 · see citing works
Machine learning, the kidney, and genotype–phenotype analysis
Sealfon, R. S. G., Mariani, L. H., Kretzler, M., & Troyanskaya, O. G. (2020) · Kidney International · doi:10.1016/j.kint.2020.02.028 · Cited by 36 · see citing works
“65 Machine learning algorithms have also been developed to categorize digital images of renal biopsy tissue.”
BackgroundDeep learning segmentation of glomeruli on kidney donor frozen sections
Davis, R., Li, X., Xu, Y., Wang, Z., Souma, N., Sotolongo, G., Bell, J., Ellis, M., Howell, D. N., Shen, X., et al. (2021) · Journal of medical imaging · doi:10.1117/1.jmi.8.6.067501 · Cited by 36 · see citing works
“The motivation behind this design choice was based on recently published work, where UNET was successfully implemented to segment glomeruli on non-frozen tissue 52–54 Nevertheless, we believe our research design is suitable for the current dataset.”
MethodHermsen, M., Ciompi, F., Adefidipe, A., Đenić, A., Dendooven, A., Smith, B. H., van Midden, D., Bräsen, J. H., Kers, J., Stegall, M. D., et al. (2022) · American Journal Of Pathology · doi:10.1016/j.ajpath.2022.06.009 · Cited by 37 · see citing works
“0 DTD AJPA3756_proof For this purpose, two previously developed CNNs aimed at the segmentation of periodic acideSchiff (PAS)estained tissue and detection of lymphocytes in immunohistochemistry (IHC) were used.(25,26) The CNNs were retrained and applied on a cohort of PAS- and CD3-stained kidney transplant biopsy slide…”
BackgroundMethodAutomatic T Staging Using Weakly Supervised Deep Learning for Nasopharyngeal Carcinoma on MR Images
Yang, Q. P., Guo, Y., Ou, X., Wang, J., & Hu, C. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27202 · Cited by 36 · see citing works
Artificial intelligence and kidney transplantation
Seyahi, N., & Özcan, Ş. G. (2021) · World Journal of Transplantation · doi:10.5500/wjt.v11.i7.277 · Cited by 36 · see citing works
Pearls and Pitfalls in Pediatric Kidney Transplantation After 5 Decades
Oomen, L., Bootsma-Robroeks, C. M., Cornelissen, E. A. M., de Wall, L. L., & Feitz, W. (2022) · Frontiers in Pediatrics · doi:10.3389/fped.2022.856630 · Cited by 36 · see citing works
“designed an application that automatically estimates glomerular filtration rate using ultrasound images and multiple studies have shown the benefits of automatic analysis of histological or radiological images (258, 259).”
MethodApplications of machine learning methods in kidney disease
Chan, L., Vaid, A., & Nadkarni, G. N. (2020) · Current Opinion in Nephrology & Hypertension · doi:10.1097/mnh.0000000000000604 · Cited by 35 · see citing works
Application of artificial intelligence in renal disease
Yao, L., Zhang, H., Zhang, M., Chen, X., Zhang, J., Huang, J., & Zhang, L. (2021) · Clinical eHealth · doi:10.1016/j.ceh.2021.11.003 · Cited by 36 · see citing works
Artificial intelligence in renal pathology: Current status and future
Feng, C., & Liu, F. (2022) · Biomolecules and Biomedicine · doi:10.17305/bjbms.2022.8318 · Cited by 35 · see citing works
“work is the first validation of quantitative results obtained by CNN with the Banff classification system [47].”
MethodArtificial intelligence: a new field of knowledge for nephrologists?
de Arizón, L. F., Ramírez, E. R. V., Teran, M. P., Perera-Lluna, A., de Maeztu, G., Nicolau, A., Furlano, M. M., & Torrá, R. (2023) · Clinical Kidney Journal · doi:10.1093/ckj/sfad182 · Cited by 33 · see citing works
Labriffe, M., Woillard, J., Gwinner, W., Braesen, J.-H., Anglicheau, D., Rabant, M., Koshy, P. J., Naesens, M., & Marquet, P. (2022) · American Journal of Transplantation · doi:10.1111/ajt.17192 · Cited by 33 · see citing works
“26 Finally, artificial intelligence may help to standardize and facilitate the interpretation of complex clinical situations, such as those grouped under the terms “kidney graft rejection”.”
BackgroundIdentify glomeruli in human kidney tissue images using a deep learning approach
Shubham, S., Jain, N., Gupta, V., Mohan, S., Ariffin, M. M., & Ahmadian, A. (2021) · Soft Computing · doi:10.1007/s00500-021-06143-z · Cited by 32 · see citing works
“In the same year, (Hermsen et al. 2019) trained a network using multiclass annotations from over 40 whole-slide images consisting of stained kidney transplant biopsies for implementation over four different datasets.”
BackgroundVerma, A., Chitalia, V. C., Waikar, S. S., & Kolachalama, V. B. (2021) · Kidney Medicine · doi:10.1016/j.xkme.2021.04.012 · Cited by 31 · see citing works
Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance
Yang, Y., Sun, K., Gao, Y., Wang, K., & Yu, G. (2023) · Diagnostics · doi:10.3390/diagnostics13193115 · Cited by 31 · see citing works
“…have increasingly employed annotations involving bounding This method involves precisely delineating the location or boundaries of target tissues or cells, effectively connecting individual image pixels with specific targets. boxes [104] and points [103,105] as alternatives to fine annotation.”
BackgroundMethodYi, Z., Xi, C., Menon, M. C., Cravedi, P., Tedla, F. M., Soto, A., Sun, Z., Liu, K., Zhang, J., Wei, C., et al. (2023) · Kidney International · doi:10.1016/j.kint.2023.09.031 · Cited by 30 · see citing works
Eccher, A., Girolami, I., Brunelli, M., Novelli, L., Mescoli, C., Malvi, D., D’Errico, A., Luchini, C., Furian, L., Zaza, G., et al. (2020) · Transplantation Reviews · doi:10.1016/j.trre.2020.100562 · Cited by 30 · see citing works
Improving unsupervised stain-to-stain translation using self-supervision and meta-learning
Bouteldja, N., Klinkhammer, B. M., Schlaich, T., Boor, P., & Merhof, D. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100107 · Cited by 30 · see citing works
“In total, three groups have recently reported the feasibility of segmenting major renal structures using CNNs, [23-25] more precisely U-nets [26] with modified architectures.”
BackgroundInfluentialPathologist Computer-Aided Diagnostic Scoring of Tumor Cell Fraction: A Swiss National Study
Frei, A. L., Oberson, R., Baumann, E., Perren, A. A., Grobholz, R., Lugli, A., Dawson, H., Abbet, C., Lertxundi, I., Reinhard, S., et al. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100335 · Cited by 29 · see citing works
Arjmandmazidi, S., Heidari, H. R., Ghasemnejad, T., Mori, Z., Molavi, L., Meraji, A., Kaghazchi, S., Aghdam, E. M., & Montazersaheb, S. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06488-1 · Cited by 27 · see citing works
“Hermsen et al. [68] successfully implemented a DL algorithm to segment kidney biopsies into anatomical components, utilizing convolutional neural networks for multiclass segmentation of digitized tissue sections.”
A fast and simple clearing and swelling protocol for 3D in-situ imaging of the kidney across scales
Unnersjö‐Jess, D., Butt, L., Höhne, M., Witasp, A., Kühne, L., Hoyer, P. F., Patrakka, J., Brinkkötter, P. T., Wernerson, A. M., Schermer, B., et al. (2020) · Kidney International · doi:10.1016/j.kint.2020.10.039 · Cited by 27 · see citing works
Bülow, R. D., Dimitrov, D., Boor, P., & Sáez-Rodríguez, J. (2021) · Seminars in Immunopathology · doi:10.1007/s00281-021-00847-y · Cited by 27 · see citing works
“More recently, semantic multiclass segmentation of kidney histology was developed by several groups [72, 96, 97].”
BackgroundResultInfluentialArtificial Intelligence Advances in Transplant Pathology
Rahman, M. A., Yilmaz, I., Albadri, S. T., Salem, F. E., Dangott, B. J., Taner, C. B., Nassar, A., & Akkus, Z. (2023) · Bioengineering · doi:10.3390/bioengineering10091041 · Cited by 27 · see citing works
“[32] employed a UNet [38] architectural CNN model for multiclass segmentation of digitized kidney biopsy tissue sections with periodic acid-Schiff (PAS) staining.”
BackgroundMethodDeep-learning model for evaluating histopathology of acute renal tubular injury
Nguyen, T. T. U., Nguyen, A.-T., Kim, H., Jung, Y. J., Park, W. S., Kim, K. M., Park, I., & Kim, W. B. (2024) · Scientific Reports · doi:10.1038/s41598-024-58506-9 · Cited by 26 · see citing works
“Another CNN for the multiclass segmentation of kidney sections with PAS staining was developed by Hermsen et al. 30 .”
MethodYamaguchi, R., Kawazoe, Y., Shimamoto, K., Shinohara, E., Tsukamoto, T., Shintani‐Domoto, Y., Nagasu, H., Uozaki, H., Ushiku, T., Nangaku, M., et al. (2020) · Kidney International Reports · doi:10.1016/j.ekir.2020.11.037 · Cited by 26 · see citing works
“7% of all glomeruli.(10) Regarding glomerular classification, George et al.”
BackgroundThe potential of artificial intelligence-based applications in kidney pathology
Bülow, R. D., Marsh, J. N., Swamidass, S. J., Gaut, J. P., & Boor, P. (2022) · Current Opinion in Nephrology & Hypertension · doi:10.1097/mnh.0000000000000784 · Cited by 27 · see citing works
Artificial intelligence in nephropathology
Boor, P. (2019) · Nature Reviews Nephrology · doi:10.1038/s41581-019-0220-x · Cited by 26 · see citing works
Cheungpasitporn, W., Athavale, A. M., Ghazi, L., KASHANI, K. B., Colicchio, T. K., Koyner, J. L., Chen, J., Ix, J. H., Nadkarni, G. N., & Neyra, J. A. (2026) · Clinical Kidney Journal · doi:10.1093/ckj/sfag004 · Cited by 31 · see citing works
“CNNs and U-Net architectures have achieved intersection-over-union scores exceeding 0.89 for renal structure segmentation [80 –82 ].”
InfluentialHacking, S. M., & Bijol, V. (2021) · Ultrastructural Pathology · doi:10.1080/01913123.2021.1882628 · Cited by 25 · see citing works
“The first convolutional neural network for multiclass segmentation was validated on PAS-stained nephrectomy samples and transplant biopsies.(5) Despite recent advancements, models like these really have no utility for clinical practice.”
MethodImage Analysis Pipeline for Renal Allograft Evaluation and Fibrosis Quantification
Farris, A. B., Vizcarra, J. C., Amgad, M. T., Cooper, L. A. D., Gutman, D. A., & Hogan, J. (2021) · Kidney International Reports · doi:10.1016/j.ekir.2021.04.019 · Cited by 25 · see citing works
Boundary-aware glomerulus segmentation: Toward one-to-many stain generalization
da Silva, J. B. B., Souza, L., Chagas, P., Calumby, R. T., de Souza, B. B. S., Pontes, I., Duarte, A. A., Pinheiro, N. D. F., dos‐Santos, W. L. C., & de Oliveira, L. R. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102104 · Cited by 25 · see citing works
Asghar, M. S., Đenić, A., Mullan, A. F., Moustafa, A., Barisoni, L. M. C., Alexander, M. P., Stegall, M. D., Augustine, J. J., Leibovich, B. C., Thompson, R. H., et al. (2023) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000000000000171 · Cited by 25 · see citing works
Deep learning automation of MEST-C classification in IgA nephropathy
Jaugey, A., Maréchal, E., Tarris, G., Paindavoine, M., Martin, L., Chabannes, M., de la Vega, M. F., Chaintreuil, M., Robier, C., Ducloux, D., et al. (2023) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfad039 · Cited by 24 · see citing works
Timakova, A., Ananev, V. V., Fayzullin, A. L., Makarov, V. V., Иванова, Е., Shekhter, A. B., & Timashev, P. S. (2023) · Biomolecules · doi:10.3390/biom13091327 · Cited by 24 · see citing works
“Deep Learning–Based Histopathologic Assessment of Kidney Tissue [24] Kidney U-net—object detection model 0.”
BackgroundArtificial intelligence driven next-generation renal histomorphometry
Santo, B. A., Rosenberg, A. Z., & Sarder, P. (2020) · Current Opinion in Nephrology & Hypertension · doi:10.1097/mnh.0000000000000598 · Cited by 24 · see citing works
Advantages of Using a Web-based Digital Platform for Kidney Preimplantation Biopsies
Neri, F., Eccher, A., Rigotti, P., Girolami, I., Zaza, G., Gambaro, G., Mastrosimini, M. G., Bencini, G., Bella, C. D., Mescoli, C., et al. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_23_21 · Cited by 23 · see citing works
“,[32] AI tool for preimplantation kidney biopsy exists”
MethodStain-Independent Deep Learning–Based Analysis of Digital Kidney Histopathology
Bouteldja, N., Hölscher, D. L., Klinkhammer, B. M., Buelow, R. D., Lotz, J., Weiss, N., Daniel, C., Amann, K., & Boor, P. (2022) · American Journal Of Pathology · doi:10.1016/j.ajpath.2022.09.011 · Cited by 23 · see citing works
Pfenning, M. B., Schmitz, J., Scheffner, I., Schulte, K., Khalifa, A. A., Tezval, H., Weidemann, A., Kulschewski, A., Kunzendorf, U., Dietrich, S., et al. (2022) · Kidney International Reports · doi:10.1016/j.ekir.2022.11.015 · Cited by 23 · see citing works
Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects
Wang, Y., Wen, Q., Jin, L., & Chen, W. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11164918 · Cited by 22 · see citing works
“84 on 2 datasets) and the source of samples (biopsy/nephrectomy) [31].”
BackgroundĐenić, A., Gaddam, M., Moustafa, A., Mullan, A. F., Luehrs, A. C., Sharma, V., Thompson, R. H., Smith, M. L., Alexander, M. P., Lerman, L. O., et al. (2023) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000000000000180 · Cited by 22 · see citing works
Decoding pathology: the role of computational pathology in research and diagnostics
Hölscher, D. L., & Bülow, R. D. (2024) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-024-03002-2 · Cited by 21 · see citing works
“In recent years, considerable efforts have been made to accurately segment these structures in human and animal specimens for different histological stains, laying the groundwork for histomorphometry analysis [13, 44, 45, 50].”
- 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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