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Publication
Computational Segmentation and Classification of Diabetic Glomerulosclerosis.
Authors Ginley B, Lutnick B, Jen KY, Fogo AB, Jain S, Rosenberg A, Walavalkar V, Wilding
G, Tomaszewski JE, Yacoub R, Rossi GM, Sarder P
Submitted By Submitted Externally on 5/28/2020
Status Published
Journal Journal of the American Society of Nephrology : JASN
Year 2019
Date Published 10/1/2019
Volume : Pages 30 : 1953 - 1967
PubMed Reference 31488606
Abstract Pathologists use visual classification of glomerular lesions to assess samples
from patients with diabetic nephropathy (DN). The results may vary among
pathologists. Digital algorithms may reduce this variability and provide more
consistent image structure interpretation., We developed a digital pipeline to
classify renal biopsies from patients with DN. We combined traditional image
analysis with modern machine learning to efficiently capture important
structures, minimize manual effort and supervision, and enforce biologic prior
information onto our model. To computationally quantify glomerular structure
despite its complexity, we simplified it to three components consisting of
nuclei, capillary lumina and Bowman spaces; and Periodic Acid-Schiff positive
structures. We detected glomerular boundaries and nuclei from whole slide images
using convolutional neural networks, and the remaining glomerular structures
using an unsupervised technique developed expressly for this purpose. We defined
a set of digital features which quantify the structural progression of DN, and a
recurrent network architecture which processes these features into a
classification., Our digital classification agreed with a senior pathologist
whose classifications were used as ground truth with moderate Cohen's kappa ? =
0.55 and 95% confidence interval [0.50, 0.60]. Two other renal pathologists
agreed with the digital classification with ?1 = 0.68, 95% interval [0.50, 0.86]
and ?2 = 0.48, 95% interval [0.32, 0.64]. Our results suggest computational
approaches are comparable to human visual classification methods, and can offer
improved precision in clinical decision workflows. We detected glomerular
boundaries from whole slide images with 0.93±0.04 balanced accuracy, glomerular
nuclei with 0.94 sensitivity and 0.93 specificity, and glomerular structural
components with 0.95 sensitivity and 0.99 specificity., Computationally derived,
histologic image features hold significant diagnostic information that may
augment clinical diagnostics.




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