TITLE:
Characterizing Placental Surface Shape with a High-Dimensional Shape Descriptor
AUTHORS:
Jen-Mei Chang, Amy Mulgrew, Carolyn Salafia
KEYWORDS:
Signed Deviation Vector; Placenta; Shape Analysis; Linear Discriminant Analysis; Principal Component Analysis
JOURNAL NAME:
Applied Mathematics,
Vol.3 No.9,
September
27,
2012
ABSTRACT: The human placenta nourishes the growing fetus during pregnancy. The newly developing field of placenta analysis seeks to understand relationships between the health of a placenta and the health of the baby. Previous studies have shown that the median placental chorionic shape at term is round, and deviation from such prototypical shape is related to a decreased placental functional efficiency. In this study, we propose the use of a nearly-continuous shape descriptor termed signed deviation vector to systematically study the relationship between various maternal and fetal characteristics and the shape of the placental surface. The proposed shape descriptor measures the amount of deviation along with the direction of the deviation a placental shape has away from the shape of normality. Using Linear Discriminant Analysis, we can independently examine how much of the placental shape is affected by maternal, newborn, and placental characteristics. The results allow us to understand how significantly various maternal and fetal conditions affect the overall shape of the placenta growth. Though the current study is largely exploratory, the initial findings indicate significant relationships between shape of the placental surface and newborn’s birth weight as well as their gestational age.