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Computer-aided assessment of regional abdominal fat with food residue removal in CT

机译：CT中食物残留物中胃部脂肪的计算机辅助评估

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摘要

Rationale and Objectives: Separate quantification of abdominal subcutaneous and visceral fat regions is essential to understand the role of regional adiposity as risk factor in epidemiological studies. Fat quantification is often based on computed tomography (CT) because fat density is distinct from other tissue densities in the abdomen. However, the presence of intestinal food residues with densities similar to fat may reduce fat quantification accuracy. We introduce an abdominal fat quantification method in CT with interest in food residue removal. Materials and Methods: Total fat was identified in the feature space of Hounsfield units and divided into subcutaneous and visceral components using model-based segmentation. Regions of food residues were identified and removed from visceral fat using a machine learning method integrating intensity, texture, and spatial information. Cost-weighting and bagging techniques were investigated to address class imbalance. Results: We validated our automated food residue removal technique against semimanual quantifications. Our feature selection experiments indicated that joint intensity and texture features produce the highest classification accuracy at 95%. We explored generalization capability using k-fold cross-validation and receiver operating characteristic (ROC) analysis with variable k. Losses in accuracy and area under ROC curve between maximum and minimum k were limited to 0.1% and 0.3%. We validated tissue segmentation against reference semimanual delineations. The Dice similarity scores were as high as 93.1 for subcutaneous fat and 85.6 for visceral fat. Conclusions: Computer-aided regional abdominal fat quantification is a reliable computational tool for large-scale epidemiological studies. Our proposed intestinal food residue reduction scheme is an original contribution of this work. Validation experiments indicate very good accuracy and generalization capability.
机译：理由和目标：腹部皮下和内脏脂肪地区的独立量化有必要了解局部减肥的作用，在流行病学研究中的风险因素。脂肪定量通常是基于计算机断层摄影（CT），因为脂肪密度是从在腹部其他组织密度不同。然而，肠道食物残渣的存在类似于脂肪的密度可以减少脂肪的量化精度。我们在CT引入腹部脂肪定量方法与食物残渣去除的兴趣。材料与方法：总脂肪中的菲尔德单位特征空间识别和使用基于模型的分割分为皮下和内脏成分。食物残渣的区域被确定和使用机器学习方法积分强度，纹理和空间信息从内脏脂肪去除。成本的加权和装袋技术进行了研究，以地址类不平衡。结果：我们证实了我们的自动化的食物残渣清除技术对semimanual的量化。我们的特征选择实验表明，关节强度和纹理特征产生最高的分类精度为95％。我们使用的k折交叉验证和接收器操作特性（ROC）与变量k分析探索泛化能力。在精度和面积损失的最大和最小K的ROC曲线下被限制在0.1％和0.3％。我们对验证参考semimanual delineations组织分割。骰子相似性分数分别高达93.1为皮下脂肪和85.6为内脏脂肪。结论：计算机辅助区域腹部脂肪定量是大规模流行病学研究可靠的计算工具。我们提出的肠道的食物残渣减少方案是这项工作的原创性贡献。验证实验表明很好的精度和泛化能力。

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  • 来源
    《Academic radiology》 |2013年第11期|共9页
  • 作者

    MakrogiannisS.; CaturegliG.; DavatzikosC.; FerrucciL.;

  • 作者单位

    Longitudinal Studies Section National Institute on Aging National Institutes of Health 3001;

    Longitudinal Studies Section National Institute on Aging National Institutes of Health 3001;

    Section of Biomedical Image Analysis Department of Radiology University of Pennsylvania;

    Longitudinal Studies Section National Institute on Aging National Institutes of Health 3001;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 放射医学;
  • 关键词

    Body composition assessment; False positive reduction;

    机译：身体成分评估;假阳性减少;

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