Examining structural patterns and causality in diabetic nephropathy using inter-glomerular distance and Bayesian graphical models

Aurijoy Majumdar, Kuang Yu Jen, Sanjay Jain, John E. Tomaszewski, Pinaki Sarder

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationDigital Pathology
EditorsJohn E. Tomaszewski, Aaron D. Ward
PublisherSPIE
ISBN (Electronic)9781510625594
DOIs
StatePublished - 2019
EventMedical Imaging 2019: Digital Pathology - San Diego, United States
Duration: Feb 20 2019Feb 21 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10956
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Digital Pathology
Country/TerritoryUnited States
CitySan Diego
Period02/20/1902/21/19

Keywords

  • Diabetic nephropathy
  • Dynamic Bayesian Network
  • Graphical Models
  • Medical Image processing
  • Minimum Spanning Tree
  • Support Vector Machine
  • Whole slide image analysis

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