Whole chorionic surface vessel feature analysis with the Boruta method, and autism risk

Chang, Jen-Mei; Chang, Ya-Mei; Han, Ruxu; Zeng, Hui; Shah, Ruchit; Newschaffer, Craig J.; Miller, Richard K.; Katzman, Philip J.; Moye, John; Salafia, Carolyn

doi:10.1016/j.placenta.2016.06.054

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“…Recent research has also demonstrated connections between placental features to the health and development of newborn babies (Yampolsky et al, 2009;Chang et al, 2012;Kaneti et al, 2013;Salafia et al, 2010;2013b). For example, placental chorionic surface shapes and vascular networks features (e.g., mean vessel thickness, mean vessel tortuosity, and number of branch points) have been linked to immediate neonatal outcomes such as birth weight after adjustment for gestational length (Yampolsky et al, 2009;Chang et al, 2012) and risk for Autism Spectrum Disorder (ASD; Salafia et al, 2012;2013a;Shah et al, 2016;Chang et al, 2016). In particular, PCSVNs associated with placentas of high-risk ASD pregnancies generally had fewer branch points, thicker and less tortuous vessels, better extension to the surface boundary, and smaller branch angles than their population-based counterparts .…”

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confidence: 99%

A shape-context model for matching placental chorionic surface vascular networks

Farnell

Chang

et al. 2018

Image Anal Stereol

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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.

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