Pixel-wise body composition prediction with a multi-task conditional generative adversarial network.
Pixel-wise body composition prediction with a multi-task conditional generative adversarial network.
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DOI：
10.1016/j.jbi.2021.103866
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发表时间：
2021-08
影响因子：
4.5
通讯作者：
Hahn J
中科院分区：
文献类型：
--
作者：
Wang Q;Xue W;Zhang X;Jin F;Hahn J
人体成分分析在健康管理和疾病预防中起着至关重要的作用。然而，目前的医疗技术，以准确地评估身体组成，如双能X射线吸收，计算机断层扫描，磁共振成像具有成本过高或电离辐射的缺点。近来，使用身体扫描仪和深度相机的基于身体形状的技术已经为通过智能地分析身体形状描述符来改进身体组成估计带来了新的机会。在本文中，我们提出了一种多任务深度神经网络方法，该方法利用条件生成对抗网络来预测仅使用3D身体表面的像素级身体组成。所提出的方法可以预测2D皮下和内脏脂肪地图在一个单一的网络，具有较高的精度。我们进一步介绍了一个解释补丁优化的纹理精度的二维脂肪图。在TCIA和LiTS数据集上的实验证明了该方法的有效性。我们提出的方法优于竞争方法至少41.3%的全身脂肪百分比，33.1%的皮下和内脏脂肪百分比，4.1%的区域脂肪预测。
The analysis of human body composition plays a critical role in health management and disease prevention. However, current medical technologies to accurately assess body composition such as dual energy X-ray absorptiometry, computed tomography, and magnetic resonance imaging have the disadvantages of prohibitive cost or ionizing radiation. Recently, body shape based techniques using body scanners and depth cameras, have brought new opportunities for improving body composition estimation by intelligently analyzing body shape descriptors. In this paper, we present a multi-task deep neural network method utilizing a conditional generative adversarial network to predict the pixel level body composition using only 3D body surfaces. The proposed method can predict 2D subcutaneous and visceral fat maps in a single network with a high accuracy. We further introduce an interpreted patch discriminator which optimizes the textural accuracy of the 2D fat maps. The validity and effectiveness of our new method are demonstrated experimentally on TCIA and LiTS datasets. Our proposed approach outperforms competitive methods by at least 41.3% for the whole body fat percentage, 33.1% for the subcutaneous and visceral fat percentage, and 4.1% for the regional fat predictions.
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