Department of Radiology, Children's Hospital Los Angeles, University of Southern California, Los Angeles, California 90027, USA.
J Magn Reson Imaging. 2011 Oct;34(4):866-73. doi: 10.1002/jmri.22690. Epub 2011 Jul 18.
To develop a computerized image analysis method to assess the quantity and distribution of abdominal fat tissues in an obese (ob/ob) mouse model relevant to 7 T magnetic resonance imaging (MRI).
A novel segmental shape model is presented that separates visceral adipose tissue (VAT) from subcutaneous adipose tissue (SAT). With shape and distance constraints, it deforms a contour inwards from the skin to the muscle wall and separates the connecting adipose tissues in an ob/ob mouse. The fat tissues are segmented by the adaptive fuzzy C means method to compensate for intensity variation in adipose images. The results were obtained by logical operations applied on the extracted fat images and the separated adipose masks.
The method was validated by manual segmentations on 109 axial slice images from 7 ob/ob mice. The average correlation coefficients of measured sizes between the automatic and manual results for total adipose tissue (TAT) is 0.907; SAT is 0.944; VAT is 0. 950. The average Dice coefficient of their positions for TAT is 0.941, SAT is 0.935, and VAT is 0.920.
The automated results correlate well with manual segmentations and the method can be used to increase laboratory automation.
开发一种计算机化的图像分析方法，以评估与 7T 磁共振成像（MRI）相关的肥胖（ob/ob）小鼠模型中腹部脂肪组织的数量和分布。
提出了一种新的分段形状模型，可将内脏脂肪组织（VAT）与皮下脂肪组织（SAT）分开。通过形状和距离约束，从皮肤向内变形轮廓并分离 ob/ob 小鼠中的连接脂肪组织。通过自适应模糊 C 均值方法对脂肪图像进行分割，以补偿脂肪图像的强度变化。通过对提取的脂肪图像和分离的脂肪掩模应用逻辑运算来获得结果。
通过对 7 只 ob/ob 小鼠的 109 个轴向切片图像进行手动分割来验证该方法。自动和手动测量总脂肪组织（TAT）、SAT 和 VAT 的大小之间的平均相关系数分别为 0.907、0.944 和 0.950。它们位置的平均 Dice 系数分别为 0.941、0.935 和 0.920。
自动分析结果与手动分割结果相关性良好，该方法可用于增加实验室自动化。
