Purushothaman Dharma, Vanselow Barbara A, Wu Shu-Biao, Butler Sarah, Brown Wendy Yvonne
School of Environmental and Rural Science, Department of Animal Science, University of New England, Armidale, NSW 2351, Australia.
NSW Department of Primary Industries, Beef Industry Centre, University of New England, Armidale, NSW 2351, Australia.
J Vet Med. 2013;2013:610654. doi: 10.1155/2013/610654. Epub 2013 Oct 10.
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 (P < 0.01). 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.
使用计算机断层扫描（CT）评估犬类肥胖情况存在局限性。传统的CT图像分析繁琐，且使用需要手动计算的预测方程。为克服这一问题，我们的研究调查了使用先进图像分析软件程序来确定犬类身体成分，并将其应用于犬类肥胖研究。选择比格犬和灵缇犬是因其形态和肥胖倾向存在差异。对六只比格犬和六只灵缇犬进行了全身CT扫描，扫描间隔固定，这些犬接受了为期28天的体重增加方案。使用OsiriX、ImageJ和AutoCAT软件程序分析在第0天和第28天获得的CT图像。CT扫描技术能够区分犬类的骨骼、瘦肉和脂肪组织，并且证明在短时间体重增加期间对检测瘦肉和脂肪的增加足够敏感。在第0天和第28天，两个品种之间的瘦肉与脂肪比率均存在显著差异（P < 0.01）。因此，CT和先进图像分析在当前研究中被证明可用于估计犬类身体成分，并且有潜力用于犬类肥胖研究。
