Hyperspectral evaluation of vasculature in induced peritonitis mouse models

Abstract

Imaging of blood vessel structure in combination with functional information about blood oxygenation can be important in characterizing many different health conditions in which the growth of new vessels contributes to the overall condition. In this paper, we present a method for extracting comprehensive maps of the vasculature from hyperspectral images that include tissue and vascular oxygenation. We also show results from a preclinical study of peritonitis in mice. First, we analyze hyperspectral images using Beer-Lambert exponential attenuation law to obtain maps of hemoglobin species throughout the sample. We then use an automatic segmentation algorithm to extract blood vessels from the hemoglobin map and combine them into a vascular structure-oxygenation map. We apply this methodology to a series of hyperspectral images of the abdominal wall of mice with and without induced peritonitis. Peritonitis is an inflammation of peritoneum that leads, if untreated, to complications such as peritoneal sclerosis and even death. Characteristic inflammatory response can also be accompanied by changes in vasculature, such as neoangiogenesis. We demonstrate a potential application of the proposed segmentation and processing method by introducing an abnormal tissue fraction metric that quantifies the amount of tissue that deviates from the average values of healthy controls. It is shown that the proposed metric successfully discriminates between healthy control subjects and model subjects with induced peritonitis and has a high statistical significance.