Table 1.
AI methods in renal pathology
| Methodology | Authors | Journal | Year | Tools | Applied task | Number of WSIs or cases |
|---|---|---|---|---|---|---|
| Classification | Ginley et al. [43] | J. Am. Soc. Nephrol. | 2019 | RNN, CNN | Glomerulus classification, Multiclass segmentation of renal morphology | 54 WSIs, human 25 WSIs, mouse |
| Uchino et al. [44] | International Journal of Medical Informatics | 2020 | CNN | Glomerulus classification | 283 cases, human | |
| Ginley et al. [55] | Proc SPIE Int Soc Opt Eng | 2020 | RNN, CNN | Glomerulus segmentation, Recurrent biopsy classification, Glomerular component analysis | 82 WSIs (65 cases), human | |
| Zee et al. [56] | Arch Pathol Lab Med | 2018 | Scoring System | Glomerulus morphology assessment | 236 WSIs (glomeruli), human | |
| Chagas et al. [57] | Artif Intell Med. | 2020 | CNN, SVM | Glomerulus hypercellularity | 811 WSIs (glomeruli), human | |
| Ledbetter et al. [58] | arXiv. | 2017 | CNN | Prediction of kidney function | 80 cases, human | |
| Detection | Temerinac-Ott et al. [45] | Proc Int Symp Image Signal Process Anal | 2017 | CNN, HOG | Glomerulus detection | 6 cases, human |
| Bukowy et al. [46] | J Am Soc Nephrol | 2018 | CNN | Glomerulus detection | 87 WSIs, human | |
| Simon et al. [47] | Sci Rep | 2018 | SVM, CNN | Glomerulus detection | 15 WSIs, mouse 25 WSIs, human |
|
| Marée et al. [48] | Proc IEEE Int Symp Biomed Imaging | 2016 | Commercial software | Glomerulus detection | 200 WSIs, human | |
| Kato et al. [61] | Bmc Bioinformatics | 2015 | SVM, HOG | Glomerulus detection | 20 WSIs, mouse | |
| Gallego et al. [62] | J Imaging | 2018 | CNN | Glomerulus detection, Glomerulus classification | 108 WSIs, human | |
| Yang et al. [91] | Med Image Comput Comput Assist Interv | 2020 | CNN | Glomerulus detection | 42 WSIs, human | |
| Segmentation | Hermsen et al. [31] | J Am Soc Nephrol | 2019 | CNN | Multiclass segmentation of nephrectomy and transplant biopsies | 50 WSIs, human |
| Ginley et al. [43] | J Am Soc Nephrol | 2019 | RNN, CNN | Glomerulus segmentation, Multiclass segmentation of renal morphology | 54 WSIs, human 25 WSIs, mouse |
|
| Bueno et al. [49] | Comput Methods Programs Biomed | 2020 | CNN | Glomerulus segmentation, Glomerulus classification | 47 WSIs, human | |
| Kannan et al. [50] | Kidney Int Rep | 2019 | CNN | Glomerulus segmentation | 275 WSIs (171 cases), human | |
| Gadermayr et al. [51] | arXiv preprint | 2017 | CNN | Glomerulus segmentation | 24 WSIs, mouse | |
| Gadermayr et al. [52] | Comput Biol Med | 2017 | SVM | Glomerulus detection, Glomerulus segmentation | 8 WSIs from mouse kidneys | |
| Ginley et al. [53] | arXiv preprint | 2020 | CNN | Multiclass segmentation of renal morphology | 65 cases, human | |
| Santo et al. [67] | Proc SPIE Int Soc Opt Eng | 2020 | Feature engineering | LN biopsies segmentation | 21 WSIs, human | |
| Gupta et al. [68] | Proc Machine Learning Res | 2019 | CNN | Glomerulus segmentation | 22 WSIs, mouse | |
| Ginley et al. [69] | J Med Imaging | 2017 | Gabor Filterbank, Statistical Testing | Glomerulus segmentation | 1000 images, mouse | |
| Sarder et al. [70] | Proc SPIE Int Soc Opt Eng | 2020 | Gabor Filterbank, Statistical Testing | Glomerulus segmentation | 15 WSIs, mouse | |
| Tey et al. [71] | Comput Methods Programs Biomed | 2018 | Alternating Decision Trees, SVM | Glomerulus segmentation | 286 WSIs (70 cases), human | |
| Altini et al. [72] | Electronics | 2020 | CNN | Glomerulus segmentation, Glomerulus classification | 26 WSIs (19 cases), human | |
| Bouteldja et al. [73] | J Am Soc Nephrol | 2020 | CNN | Kidney tissue segmentation | 168 WSIs, mouse | |
| Jha et al. [92] | J Med Imaging | 2020 | CNN | Glomerulus segmentation | 1454 images, human | |
| Jayapandian et al. [74] | Kidney Int | 2020 | CNN | Kidney tissue segmentation | 459 WSIs (125 cases), human | |
| Synthesis | Murali et al. [78] | Proc SPIE Int Soc Opt Eng | 2020 | GAN | Renal histopathology images synthesis | 20K images, human |
| Lutnick et al. [79] | Proc SPIE Int Soc Opt Eng. | 2020 | VAE-GAN | Glomerulus images synthetic, Glomerulus classification | 59930 images, human 27508 images, mouse |
|
| Zhang et al. [81] | Light: Sci & Appl | 2020 | GAN | Digital stain | 12 WSIs, human | |
| Gadermayr et al. [82] | Med Image Comput Comput Assist Interv | 2018 | GAN, CNN | Digital stain, Glomerulus segmentation | 59 WSIs, mouse | |
| Gadermayr et al. [83] | IEEE Trans Med Imaging | 2019 | GAN, CNN | Digital stain, Glomerulus segmentation | 41 WSIs, mouse | |
| de Bel et al. [84] | Med Imaging Deep Learning | 2020 | GAN | Digital stain | 64 WSIs, human | |
| Wu et al. [86] | Proc Conf AAAI Artif Intell | 2019 | GAN, CNN | Digital stain, Glomerulus classification | 209 cases, human | |
| Gadermayr et al. [88] | Med Imaging Deep Learning | 2019 | GAN | Glomerulus synthetic segmentation | 23 WSIs, mouse | |
| Gupta et al. [89] | Med Image Comput Comput Assist Interv | 2019 | GAN, CNN | Digital stain, Glomerulus segmentation | 59 WSIs, mouse | |
| Mei et al. [90] | IEEE ICASSP | 2019 | GAN, CNN | Digital stain, Glomerulus segmentation | 819 images, human |
*
CNN = convolutional neural network, GAN = generative adversarial network, VAE = variational autoencoder, SVM = support vector machine, RNN = recurrent neural network, HOG = histogram of oriented gradients.
*
WSI = whole slide imaging, LN = lupus nephritis.