Automated adipose tissue detection method

Obesity or adiposity is a major global health problem. Adipose tissue has been subdivided into two types: white adipose tissue (WAT) that stores energy, and brown adipose tissue (BAT) that functions to dissipate energy in the form of heat. BAT plays a particularly important role in newborns, which use this tissue to defend themselves against cold. However, using glucose tracer, it has been independently demonstrated that metabolically active BAT exists in adults. This has revived a scientific interest in BAT, targeting BAT as a new method to reduce obesity. However, BAT is extremely difficult to visualize, and even more to quantify. That is why we developed a mathematical model and intelligent software capable of automatically detecting and analyzing BAT, with the goal of quantifying it in the mammary adipose microenvironment. We utilized an animal model with abnormal mammary adipose tissue environment with increased amount of BAT in comparison with normal animals for this analysis. The proposed algorithm has two-fold function: visual and numerical. Visual function is expressed through detection and indication of the potential brown fat on the tissue microscopic images by marking it in a different color and distinguishing it from the rest of the tissue. Numerical function enables quantification of the amount of detected brown fat by calculating its numerical equivalent.