Computational Segmentation and Classification of Diabetic Glomerulosclerosis

Abstract

BackgroundPathologists 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.MethodsWe 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.ResultsOur digital classification agreed with a senior pathologist whose classifications were used as ground truth with moderate Cohen’s kappa κ = 0.55 and 95% confidence interval [0.50, 0.60]. Two other renal pathologists agreed with the digital classification with κ1 = 0.68, 95% interval [0.50, 0.86] and κ2 = 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.ConclusionsComputationally derived, histologic image features hold significant diagnostic information that may augment clinical diagnostics.

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

“…AI-based clinical decision support systems (CDSS) can be implemented employing the expert system strategy, data-driven approach, or an ensemble approach by coupling both. An expert system consolidates a knowledge base containing a set of rules for specific clinical scenarios, and the initial rule set may be acquired from domain experts or learned from data through machine learning algorithms [72,[78][79][80] AI has recently been adopted for the prediction, diagnosis, and treatment of kidney diseases [76,[81][82][83][84][85], as shown in Table 2. For example, a prediction model based on the combination of a machine learning algorithm and survival analysis has recently developed and can stratify risk for kidney disease progression among patients with IgA Nephropathy [86].…”

“…AI has recently been adopted for the prediction, diagnosis, and treatment of kidney diseases [76,[81][82][83][84][85], as shown in Table 2. For example, a prediction model based on the combination of a machine learning algorithm and survival analysis has recently developed and can stratify risk for kidney disease progression among patients with IgA Nephropathy [86].…”

Semantic Segmentation Framework for Glomeruli Detection and Classification in Kidney Histological Sections

“…In the realm of digital pathology, several recent studies have proposed CAD systems for glomerulus identification and classification in renal biopsies [1][2][3][4][5][6][7][8]. The eligibility for transplantation of a kidney retrieved from Expanded Criteria Donors (ECD) relies on rush histological examination of the organ to evaluate suitability for transplant [9].…”

“…They used a Fully Convolutional Network (FCN) followed by a blob-detection algorithm [13], based on Laplacian-of-Gaussian, to post-process the FCN probability maps into object detection predictions [8]. Ginley et al proposed a CAD to classify renal biopsies of patients with diabetic nephropathy [7], using a combination of classical image processing and novel machine learning techniques. Hermsen et al adopted CNNs, namely an ensemble of five U-Nets, for segmentation of ten tissue classes from WSIs of periodic acid-Schiff (PAS) stained kidney transplant biopsies [14].…”

“…The analysis of the literature suggests that main works focused on the glomerular detection task only, without considering the further classification into sclerotic and non-sclerotic [1,2,4,6]. Few papers considered the assessment of global glomerulosclerosis from kidney biopsies [7,8,14].…”