Automated renal histopathology: digital extraction and quantification of renal pathology

Sarder, Pinaki; Ginley, Brandon; Tomaszewski, John E.

doi:10.1117/12.2217329

Cited by 21 publications

(30 citation statements)

References 14 publications

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“…However, the immunohistological approach is less applicable for evaluation of preimplantation biopsies. Other groups have used a variety of techniques for glomerulus identification on routine stains [10], [22], [29], [30], [34], [35], typically through the use of some combination of colorspace transformation, thresholding, and/or morphological descriptors alone or as input to support vector machines or CNNs.…”

Section: Related Workmentioning

confidence: 99%

“…The remainder describe differences in descriptors in glomeruli from normal and pathologic populations, rather than isolating and labeling different glomeruli within the same wide image field. The current study [27] WSI, anti-desmin, fixed rat segmentation (normal, diabetic) [28] field, anti-nestin, fixed rat segmentation, characterization (normal, renal failure) [23] WSI, PAS, fixed mouse segmentation (normal) [25] WSI, Jones H&E/PAS/other, fixed human segmentation (normal) [22] WSI, H&E/PAS/others human, mouse, rat segmentation, characterization (normal, diabetic) [29] WSI, H&E primate segmentation, characterization (normal, diseased) [30] WSI, Masson's trichrome human segmentation - [31] field, H&E mouse segmentation - [24] patch, PAS human segmentation - [32] patch rat segmentation, characterization (normal, hypertrophy) [33] patch, H&E/PAS, fixed human segmentation, characterization (normal, proliferating) [34], [35] patch, H&E/PAS, fixed mouse, rat segmentation, characterization (normal) [36]- [38] patch -segmentation demonstrates the novel use of CNNs applied to frozen H&E sections to detect non-sclerotic and sclerotic glomeruli to assist pathologists in intra-operative interpretation of percent global glomerulosclerosis.…”

Section: Related Workmentioning

confidence: 99%

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Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections

Marsh

Matlock

Kudose

et al. 2018

Preprint

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Transplantable kidneys are in very limited supply. Accurate viability assessment prior to transplantation could minimize organ discard. Rapid and accurate evaluation of intraoperative donor kidney biopsies is essential for determining which kidneys are eligible for transplantation. The criteria for accepting or rejecting donor kidneys relies heavily on pathologist determination of the percent of glomeruli (determined from a frozen section) that are normal and sclerotic. This percentage is a critical measurement that correlates with transplant outcome. Inter-and intra-observer variability in donor biopsy evaluation is, however, significant. An automated method for determination of percent global glomerulosclerosis could prove useful in decreasing evaluation variability, increasing throughput, and easing the burden on pathologists. Here, we describe the development of a deep learning model that identifies and classifies non-sclerosed and sclerosed glomeruli in whole-slide images of donor kidney frozen section biopsies. This model extends a convolutional neural network (CNN) pre-trained on a large database of digital images. The extended model, when trained on just 48 whole slide images, exhibits slide-level evaluation performance on par with expert renal pathologists. The model substantially outperforms a model trained on image patches of isolated glomeruli. Encouragingly, the model's performance is robust to slide preparation artifacts associated with frozen section preparation. As the first model reported that identifies and classifies normal and sclerotic glomeruli in frozen kidney sections, and thus the first model reported in the literature relevant to kidney transplantation, it may become an essential part of donor kidney biopsy evaluation in the clinical setting.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections

Marsh

Matlock

Kudose

et al. 2018

Preprint

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“…2.1 and more extensively in our recent work. 19 Figure 7(b) shows the pipeline to localize individual glomeruli boundaries. Individual glomeruli images are preprocessed for Gabor filtering, and each image is filtered with a bank of Gabor filters for textural discrimination, which creates an initial mask of glomerular area.…”

Section: Comparison With the Segmental Histogram Of Orientated Gradiementioning

confidence: 99%

“…34 Glomerular biopsy location estimation is extensively discussed in Ref. 19. Each candidate region is sent to the boundary estimation method.…”

Section: Single Glomerular Location Estimation From Biopsiesmentioning

confidence: 99%

“…In the past, we have reported using Gabor filters to detect glomeruli. 19 Here, we propose an improved method, a pipeline of image processing and statistical tools working in complement to discriminate dense glomerular textural density from sparse extraglomerular textural density. Our method first uses a form of hotspot analysis for glomerular nuclear clustering to select candidate glomerular regions for a more detailed analysis.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised labeling of glomerular boundaries using Gabor filters and statistical testing in renal histology

et al. 2017

Self Cite

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The glomerulus is the blood filtering unit of the kidney. Each human kidney contains [Formula: see text] glomeruli. Several renal conditions originate from structural damage to glomerular microcompartments, such as proteinuria, the excessive loss of blood proteins into urine. The gold standard for evaluating structural damage in renal pathology is histopathological and immunofluorescence examination of needle biopsies under a light microscope. This method is limited by qualitative or semiquantitative manual scoring approaches to the evaluation of glomerular structural features. Computational quantification of equivalent features promises to improve the precision of glomerular structural analysis. One large obstacle to the computational quantification of renal tissue is the identification of complex glomerular boundaries automatically. To mitigate this issue, we developed a computational pipeline capable of extracting and exactly defining glomerular boundaries. Our method, composed of Gabor filtering, Gaussian blurring, statistical [Formula: see text]-testing, and distance transform, is able to accurately identify glomerular boundaries with mean sensitivity/specificity of [Formula: see text] and accuracy of 0.92, on [Formula: see text] glomeruli images stained with standard renal histological stains. Our method will simplify computational partitioning of glomerular microcompartments hidden within dense textural boundaries. Automatic quantification of glomeruli will streamline structural analysis in clinic and can pioneer real-time diagnoses and interventions for renal care.

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Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning

Zeng

Nan²,

et al. 2020

The Journal of Pathology

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Identification of glomerular lesions and structures is a key point for pathological diagnosis, treatment instructions, and prognosis evaluation in kidney diseases. These time‐consuming tasks require a more accurate and reproducible quantitative analysis method. We established derivation and validation cohorts composed of 400 Chinese patients with immunoglobulin A nephropathy (IgAN) retrospectively. Deep convolutional neural networks and biomedical image processing algorithms were implemented to locate glomeruli, identify glomerular lesions (global and segmental glomerular sclerosis, crescent, and none of the above), identify and quantify different intrinsic glomerular cells, and assess a network‐based mesangial hypercellularity score in periodic acid–Schiff (PAS)‐stained slides. Our framework achieved 93.1% average precision and 94.9% average recall for location of glomeruli, and a total Cohen's kappa of 0.912 [95% confidence interval (CI), 0.892–0.932] for glomerular lesion classification. The evaluation of global, segmental glomerular sclerosis, and crescents achieved Cohen's kappa values of 1.0, 0.776, 0.861, and 95% CI of (1.0, 1.0), (0.727, 0.825), (0.824, 0.898), respectively. The well‐designed neural network can identify three kinds of intrinsic glomerular cells with 92.2% accuracy, surpassing the about 5–11% average accuracy of junior pathologists. Statistical interpretation shows that there was a significant difference (P value < 0.0001) between this analytic renal pathology system (ARPS) and four junior pathologists for identifying mesangial and endothelial cells, while that for podocytes was similar, with P value = 0.0602. In addition, this study indicated that the ratio of mesangial cells, endothelial cells, and podocytes within glomeruli from IgAN was 0.41:0.36:0.23, and the performance of mesangial score assessment reached a Cohen's kappa of 0.42 and 95% CI (0.18, 0.69). The proposed computer‐aided diagnosis system has feasibility for quantitative analysis and auxiliary recognition of glomerular pathological features. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

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Automated renal histopathology: digital extraction and quantification of renal pathology

Cited by 21 publications

References 14 publications

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections

Unsupervised labeling of glomerular boundaries using Gabor filters and statistical testing in renal histology

Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning

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