Abstract

One major role of an accurate distribution of abdominal adipose tissue is to predict disease risk. This paper proposes a novel effective three-level convolutional neural network (CNN) approach to automate the selection of abdominal computed tomography (CT) images on large-scale CT scans and automatically quantify the visceral and subcutaneous adipose tissue. First, the proposed framework employs support vector machine (SVM) classifier with a configured parameter to cluster abdominal CT images from screening patients. Second, a pyramid dilation network (DilaLab) is designed based on CNN, to address the complex distribution and non-abdominal internal adipose tissue problems of biomedical image segmentation in visceral adipose tissue. Finally, since the trained DilaLab implicitly encodes the fat-related learning, the transferred DilaLab learning and a simple decoder constitute a new network (DilaLabPlus) for quantifying subcutaneous adipose tissue. The networks are trained not only all available CT images but also with a limited number of CT scans, such as 70 samples including a 10% validation subset. All networks are yielding more precise results. The mean accuracy of the configured SVM classifier yields promising performance of 99.83%, while DilaLabPlus achieves a remarkable performance improvement an with average of 98.08 ± 0.84% standard deviation and 0.7 ± 0.8% standard deviation false-positive rate. The performance of DilaLab yields average 97.82 ± 1.34% standard deviation and 1.23 ± 1.33% standard deviation false-positive rate. This study demonstrates considerable improvement in feasibility and reliability for the fully automated recognition of abdominal CT slices and segmentation of selected abdominal CT in subcutaneous and visceral adipose tissue, and it has a high agreement with a manually annotated biomarker.

中文翻译：

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在CT扫描上全自动分割皮下和内脏脂肪组织的有效CNN方法

摘要

腹部脂肪组织的准确分布的主要作用之一是预测疾病的风险。本文提出了一种新颖的有效三级卷积神经网络（CNN）方法，以自动在大规模CT扫描上自动选择腹部计算机断层摄影（CT）图像，并自动量化内脏和皮下脂肪组织。首先，所提出的框架采用具有配置参数的支持向量机（SVM）分类器来聚类来自筛查患者的腹部CT图像。其次，基于CNN设计了金字塔扩张网络（DilaLab），以解决内脏脂肪组织中生物医学图像分割的复杂分布和非腹部内部脂肪组织的问题。最后，由于受过训练的DilaLab隐式编码了与脂肪相关的学习，转移的DilaLab学习信息和简单的解码器构成了用于量化皮下脂肪组织的新网络（DilaLabPlus）。该网络不仅会训练所有可用的CT图像，而且还会训练数量有限的CT扫描，例如包含10％验证子集的70个样本。所有网络均产生更精确的结果。配置的SVM分类器的平均准确度可提供99.83％的有希望的性能，而DilaLabPlus则可实现显着的性能改善，平均标准偏差为98.08±0.84％，标准偏差的假阳性率为0.7±0.8％。DilaLab的性能平均产生97.82±1.34％标准偏差和1.23±1.33％标准偏差假阳性率。