Systemic lupus erythematosus is a disease in which the immune system attacks host tissues. One organ commonly attacked is the kidney, in which case the resultant acute and chronic damages are called lupus nephritis. The accumulated damage can result in renal failure. The percutaneous renal biopsy is invaluable to the assessment of the disease and its therapeutic response. A large portion of the pathological assessment is done by histological analysis of the biopsied tissue with light microscopy. Computational models can alleviate a portion of expert disagreement by providing unified, reproducible quantifications of digitized image structures. In this work, we perform fully automated whole slide segmentation of glomeruli from Periodic Acid- Schiff (PAS), hematoxylin and eosin, silver, and trichrome stained lupus nephritis biopsies. The automatically extracted PAS glomeruli are quantified by a set of 285 hand-crafted features designed specifically to target glomerular lesions in lupus nephritis. These features are fed in sequence to a recurrent neural network architecture which views multiple glomerular features from a single biopsy, and outputs a continuous diagnostic value representative of classes II-V of the scheme by Weening et al1. On 82 whole slide images taken from 65 patients, compared to renal pathologist annotations and using only the PAS stain, the network achieved a Cohen’s kappa of 0.42 with 95% confidence interval [0.32, 0.52] to render the correct class chosen from II-V, and 0.56, 95% CI [0.43, 0.69] to render an additional class V diagnosis when required.
Ki-67 index is an important diagnostic factor in gastrointestinal neuroendocrine tumor (GI-NET). The current gold standard for grading GI-NETs involves the visual screening of histopathologically stained tissues, for hot-spots containing high amounts of proliferating tumor cells (stained with Ki-67 antibody). Subsequently, the Ki-67 index, i.e. the percentage of proliferating tumor cells within the hot-spot is manually obtained. To automate this subjective and time consuming process, we have developed an integrated pipeline, termed SKIE (synaptophysin-Ki-67 index estimator), combining double-immunohistochemical (IHC) staining for synaptophysin (stains tumor) and Ki-67, with whole slide image (WSI) analysis. The Ki-67 index for 50 human GI-NET WSIs were estimated by SKIE and compared with three pathologists’ assessment, and the gold standard (exhaustive counting by a fourth pathologist) based on the double-stained image. All four pathologists unanimously graded 38 WSIs, among which, SKIE achieved 94.74% accuracy. One discrepant case was attributed to staining inconsistencies and the other to SKIE selecting a better hot-spot. The remaining 12 WSIs had discrepant grades among pathologists, and hence, the gold standard was chosen for comparison, wherein, 10 WSI grades matched with that of the gold standard, and SKIE assigned a lower and higher grade to two cases. Overall, SKIE agreed with the gold standard with a substantial linear weighted Cohen’s kappa κ = 0.622 with CI [0.286, 0.958]. We further expanded our method to deep-SKIE, wherein, a deep convolutional neural network (DCNN) was trained and validated using 13,736 hotspot-sized tiles from 40 WSIs, each categorized into one of four classes (background, non-tumor, tumor grade 1, tumor grade 2) by SKIE and tested on 9 WSIs. Deep-SKIE achieved an accuracy of 91.63% with near-perfect agreement (κ = 0.88 with CI [0.87, 0.89]) with the gold standard.
Diabetic Nephropathy (DN) progression is stratified into several stages with different levels of proteinuria, albuminuria, and physical characteristics as observed by pathologists. These physical changes are primarily visible within a patient’s glomeruli which function as filtration units for blood returning for oxygenation. As DN stage increases, it is possible to observe the thickening of the glomerular basement membrane, expansion of the mesangium, and development of nodular sclerosis. Classification of different stages of DN by pathologists is based on semiqualitative assessments of these characteristics on an individual glomerulus basis. Being able to probabilistically infer stage membership of individual glomeruli based on a combination of easily observable and hidden image features would be an invaluable tool for furthering our understanding of the drivers of DN progression. Markov Particle filters, included in the bnlearn package in R, were used to query a Bayesian Network (BN) constructed using the structural Hill-Climbing algorithm on a set of glomerular features. These features included both traditional characteristics such as glomerular area and number of mesangial nuclei as well as more abstract features derived from Minimum Spanning Trees (MST) to quantify spatial distribution of mesangial nuclei. Our results using images from multiple institutions suggest that these abstract features exercise a variable influence on DN stage membership over the course of disease progression. Further research incorporating clinical data will give nephrologists a “white box” visual of quantitative factors present in DN patients.
Generative modeling using GANs has gained traction in machine learning literature, as training does not require labeled datasets. This is perfect for applications in biological datasets, where large labeled datasets are often difficult and expensive to acquire. However, generative models offer no easy way to encode real images into feature-sets, something that is desirable for network explainability and may yield potentially informative image features. For this reason, we test a VAE-GAN architecture for label-free modeling of glomerular structural features. We show that this network can generate realistic looking synthetic images, and be used to interpolate between images. To prove the biological relevance of the network encodings, we classify small-labeled sets of encoded glomeruli by biopsy Tervaert class and for the presence of sclerosis, obtaining a Cohen’s kappa values of 0.87 and 0.78 respectfully.
In diabetic nephropathy (DN), hyperglycemia drives a progressive thickening of glomerular filtration surfaces, increased cell proliferation as well as mesangial expansion and a constriction of capillary lumens. This leads to progressive structural changes inside the Glomeruli. In this work, we make a study of structural glomerular changes in DN from a graph-theoretic standpoint, using features extracted from Minimal Spanning Trees (MSTs) constructed over intercellular distances in order to classify the “packing signatures” of different DN stages. We further investigate the significance of the competing effects of Volume change measured here in 2Dimensional Pixel span area (Area) on one hand and increased cell proliferation on the other in determining the packing patterns. Towards that we formulate the problem as Dynamic Bayesian Network (DBN). From our preliminary results we do postulate that volume expansion caused by internal pressure as capillary lumens constriction has perhaps has a greater effect in the early stages.
The glomerulus is the primary compartment of blood filtration in the kidney. It is a sphere of bundled, fenestrated capillaries that selectively allows solute loss. Structural damages to glomerular micro-compartments lead to physiological failures which influence filtration efficacy. The sole way to confirm glomerular structural damage in renal pathology is by examining histopathological or immunofluorescence stained needle biopsies under a light microscope. However, this method is extremely tedious and time consuming, and requires manual scoring on the number and volume of structures. Computational image analysis is the perfect tool to ease this burden. The major obstacle to development of digital histopathological quantification protocols for renal pathology is the extreme heterogeneity present within kidney tissue. Here we present an automated computational pipeline to 1) segment glomerular compartment boundaries and 2) quantify features of compartments, in healthy and diseased renal tissue. The segmentation involves a two stage process, one step for rough segmentation generation and another for refinement. Using a Naïve Bayesian classifier on the resulting feature set, this method was able to distinguish pathological stage IIa from III with 0.89/0.93 sensitivity/specificity and stage IIb from III with 0.7/0.8 sensitivity/specificity, on n = 514 glomeruli taken from n = 13 human biopsies with diagnosed diabetic nephropathy, and n = 5 human renal tissues with no histological abnormalities. Our method will simplify computational partitioning of glomerular micro-compartments and subsequent quantification. We aim for our methods to ease manual labor associated with clinical diagnosis of renal disease.
The adoption of deep learning techniques in medical applications has thus far been limited by the availability of the large labeled datasets required to robustly train neural networks, as well as difficulty interpreting these networks. However, recent techniques for unsupervised training of neural networks promise to address these issues, leveraging only structure to model input data. We propose the use of a variational autoencoder (VAE) which utilizes data from an animal model to augment the training set and non-linear dimensionality reduction to map this data to human sets. This architecture utilizes variational inference, performed on latent parameters, to statistically model the probability distribution of training data in a latent feature space. We show the feasibility of VAEs, using images of mouse and human renal glomeruli from various pathological stages of diabetic nephropathy (DN), to model the progression of structural changes which occur in DN. When plotted in a 2-dimentional latent space, human and mouse glomeruli, show separation with some overlap, suggesting that the data is continuous, and can be statistically correlated. When DN stage is plotted in this latent space, trends in disease pathology are visualized.