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18 March 2019 Examining structural patterns and causality in diabetic nephropathy using inter-glomerular distance and Bayesian graphical models
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Abstract
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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Aurijoy Majumdar, Kuang-Yu Jen, Sanjay Jain, John E. Tomaszewski, and Pinaki Sarder "Examining structural patterns and causality in diabetic nephropathy using inter-glomerular distance and Bayesian graphical models", Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 1095608 (18 March 2019); https://doi.org/10.1117/12.2513598
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