The use of computed tomography (CT) to evaluate obesity in canines is limited. Traditional CT image analysis is cumbersome and uses prediction equations that require manual calculations. In order to overcome this, our study investigated the use of advanced image analysis software programs to determine body composition in dogs with an application to canine obesity research. Beagles and greyhounds were chosen for their differences in morphology and propensity to obesity. Whole body CT scans with regular intervals were performed on six beagles and six greyhounds that were subjected to a 28-day weight-gain protocol. The CT images obtained at days 0 and 28 were analyzed using software programs OsiriX, ImageJ, and AutoCAT. The CT scanning technique was able to differentiate bone, lean, and fat tissue in dogs and proved sensitive enough to detect increases in both lean and fat during weight gain over a short period. A significant difference in lean？:？fat ratio was observed between the two breeds on both days 0 and 28 ( ). Therefore, CT and advanced image analysis proved useful in the current study for the estimation of body composition in dogs and has the potential to be used in canine obesity research. 1. Introduction Obesity is a common nutritional disorder in dogs with a reported incidence of between 22% and 40% globally [1, 2]. The most commonly used methods to evaluate body composition in canine obesity research are dual-energy X-ray absorptiometry (DXA) and deuterium oxide dilution [3]. When fat estimated by deuterium oxide dilution was validated against fat determined by ether extraction of the carcass using male and female dogs, a coefficient of determination, , was obtained [4]. When DXA methodology was validated in dogs using chemical analysis of dissected carcasses, it was found to have an overall coefficient of determination, , for fat mass; however, greater inaccuracies were observed in some individual animals mainly due to skeletal muscle hydration [5]. This was further confirmed in a more recent study in pigs [6] that evaluated the DXA methodology using whole dissection and ashing and concluded that DXA provided inaccurate and misleading results without taking into consideration the hydration and lipid content variability within tissues. A recent study investigated a potentially new method for detecting body composition in dogs: bioimpedance spectroscopy [7]. The method was validated against DXA and found good agreement with the two methods (correlation coefficient for fat) at a population level, but was limited in accuracy when used for
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