Title
Examining structural patterns and causality in diabetic nephropathy using inter-glomerular distance and Bayesian graphical models.
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.
Year
DOI
Venue
2019
10.1117/12.2513598
Proceedings of SPIE
Keywords
Field
DocType
Diabetic nephropathy,whole slide image analysis,Minimum Spanning Tree,Support Vector Machine,Graphical Models,Dynamic Bayesian Network,Medical Image processing
Diabetic nephropathy,Causality,Computer science,Artificial intelligence,Graphical model,Machine learning,Bayesian probability
Conference
Volume
ISSN
Citations 
10956
0277-786X
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Aurijoy Majumdar100.34
Kuang-Yu Jen202.03
Sanjay Jain300.34
John E. Tomaszewski419818.60
Pinaki Sarder525.79