A SHAPE-CONTEXT MODEL FOR MATCHING PLACENTAL CHORIONIC SURFACE VASCULAR NETWORKS

Elin Farnell, Shawn Farnell, Jen-Mei Chang, Madison Hoffman, Robin Belton, Kathryn Keaty, Sanford Lederman, Carolyn Salafia

Abstract

Placental chorionic surface vascular networks (PCSVNs) are essential high-capacitance, low-resistance distribution and drainage networks, and are hence important to placental function and to fetal and newborn health. It was hypothesized that variations in the PCSVN structure may reflect both the overall effects of genetic and environmentally regulated variations in branching morphogenesis within the conceptus and the fetus’s vital organs. A critical step in PCSVN analysis is the extraction of blood vessel structure, which has only been done manually through a laborious process, making studies in large cohorts and applications in clinical settings nearly impossible. The large variation in the shape, color, and texture of the placenta presents significant challenges to both machine and human to accurately extract PCSVNs. To increase the visibility of the vessels, colored paint can be injected into the vascular networks of placentas, allowing PCSVNs to be manually traced with a high level of accuracy.

This paper provides a proof-of-concept study to explain the geometric differences between manual tracings of paint-injected and un-manipulated PCSVNs under the framework of a shape-context model. Under this framework, paint-injected and un-manipulated tracings of PCSVNs can be matched with nearly 100% accuracy. The implication of our results is that the manual tracing protocol yields faithful PCSVN representations modulo a set of affine transformations, making manual tracing a reliable method for studying PCSVNs. Our work provides assurance to a new pre-processing approach for studying vascular networks by ways of dye-injection in medical imaging problems.


Keywords
altered geometry; paint-injection; placenta; shape matching; tracing

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DOI: 10.5566/ias.1708

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Image Analysis & Stereology
EISSN 1854-5165 (Electronic version)
ISSN 1580-3139 (Printed version)