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首页> 外文期刊>Journal of Digital Imaging >Automated Separation of Visceral and Subcutaneous Adiposity in In Vivo Microcomputed Tomographies of Mice
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Automated Separation of Visceral and Subcutaneous Adiposity in In Vivo Microcomputed Tomographies of Mice

机译：体内微计算机体层摄影术中小鼠内脏和皮下脂肪的自动分离

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Reflecting its high resolution and contrast capabilities, microcomputed tomography (μCT) can provide an in vivo assessment of adiposity with excellent spatial specificity in the mouse. Herein, an automated algorithm that separates the total abdominal adiposity into visceral and subcutaneous compartments is detailed. This algorithm relies on Canny edge detection and mathematical morphological operations to automate the manual contouring process that is otherwise required to spatially delineate the different adipose deposits. The algorithm was tested and verified with μCT scans from 74 C57BL/6J mice that had a broad range of body weights and adiposity. Despite the heterogeneity within this sample of mice, the algorithm demonstrated a high degree of stability and robustness that did not necessitate changing of any of the initially set input variables. Comparisons of data between the automated and manual methods were in complete agreement (R 2 = 0.99). Compared to manual contouring, the increase in precision and accuracy, while decreasing processing time by at least an order of magnitude, suggests that this algorithm can be used effectively to separately assess the development of total, visceral, and subcutaneous adiposity. As an application of this method, preliminary data from adult mice suggest that a relative increase in either subcutaneous, visceral, or total fat negatively influences skeletal quantity and that fat infiltration in the liver is greatly increased by a high-fat diet.
机译：微型计算机断层扫描（μCT）反映了其高分辨率和对比度功能，可以在小鼠体内对肥胖进行体内评估，并具有出色的空间特异性。在此，详细介绍了一种自动算法，该算法将整个腹部肥胖症分为内脏和皮下隔室。该算法依靠Canny边缘检测和数学形态学运算来自动执行手动轮廓绘制过程，否则需要在空间上描绘出不同的脂肪沉积物。通过对74具体重和肥胖范围广泛的C57BL / 6J小鼠进行μCT扫描对算法进行了测试和验证。尽管在此小鼠样本中存在异质性，该算法仍显示出高度的稳定性和鲁棒性，无需更改任何初始设置的输入变量。自动方法和手动方法之间的数据比较完全一致（R 2 = 0.99）。与手动轮廓绘制相比，精度和准确性的提高，同时将处理时间缩短了至少一个数量级，这表明该算法可有效地用于分别评估总体，内脏和皮下脂肪的发展。作为这种方法的一种应用，来自成年小鼠的初步数据表明皮下，内脏或总脂肪的相对增加会对骨骼数量产生负面影响，高脂饮食会大大增加肝脏中的脂肪渗透。

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