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Predicting the thermophysical properties of skin tumor based on the surface temperature and deep learning

机译：基于表面温度和深度学习预测皮肤肿瘤的热物理性质

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摘要

Predicting the thermophysical properties of the skin tumor is a great challenge in the field of biomedical engineering, which is helpful for the diagnostic of the tumor. In this paper, the relationship between thermophysical properties of the tumor and the time-dependent skin surface temperature could be revealed through dynamic thermography and deep learning. The deep learning model for the inverse bio-heat conduction problem is used to identify the overall thermophysical properties of the skin tumor, including the depth, size, thermal conductivity, heat generation and blood perfusion of the skin tumor. Firstly, a 3D numerical skin model with different layers, including the tumor, muscle, fat, dermis and epidermis, is constructed to calculate the surface temperature under different thermophysical properties of the skin tumor. And the numerical model is verified by comparing the time-dependent skin surface temperature of Clark Ⅱ and Clark Ⅳ tumors. Then the deep learning model is established to relate the time-dependent surface temperature with the thermophysical properties and trained by the numerical simulation data. The performances of the deep learning model are examined by the Clark Ⅱ and Clark Ⅳ tumors with different measurement errors. The results show that the deep learning model can learn the abstract features of the time-dependent surface temperature and estimate the tumor properties by the skin surface temperature. Compared with the Clark Ⅳ tumor, the measurement errors have more influence on the Clark Ⅱ tumor. At last, the influences of seven thermophysical properties of the tumor on the skin surface temperature are further numerically analyzed to understand the deep learning model predictions. Interestingly, it is found that the deep learning model can well predict the tumor heat generation and blood perfusion of the skin tumor. The numerical simulation results show that the surface temperature profiles are influenced by the properties mentioned. However, the normalized temperature variation profiles do not. The proposed method provides a useful diagnostic tool for estimating the thermophysical properties of the skin tumor.
机译：预测皮肤肿瘤的热物理性质是生物医学工程领域的巨大挑战，这有助于肿瘤的诊断。本文通过动态热成像和深度学习可以揭示肿瘤热物理性质与时间依赖性皮肤表面温度的关系。逆生物导热问题的深度学习模型用于鉴定皮肤肿瘤的整体热物理性质，包括皮肤肿瘤的深度，尺寸，导热性，发热和血液灌注。首先，构建具有不同层的3D数值皮肤模型，包括肿瘤，肌肉，脂肪，真皮和表皮，以计算皮肤肿瘤的不同热物理性质下的表面温度。通过比较克拉克Ⅱ和克拉克肿瘤的时间依赖性皮肤表面温度来验证数值模型。然后建立深度学习模型以将时间依赖性表面温度与热物理特性相关联，并通过数值模拟数据训练。克拉克Ⅱ和克拉克肿瘤检查了深度学习模型的性能，具有不同的测量误差。结果表明，深度学习模型可以学习时间依赖性表面温度的抽象特征，并通过皮肤表面温度估计肿瘤性质。与克拉克ⅳ肿瘤相比，测量误差对克拉克Ⅱ肿瘤产生更多影响。最后，进一步分析了肿瘤对皮肤表面温度的七种热物理性质的影响，以了解深度学习模型预测。有趣的是，发现深度学习模型可以很好地预测皮肤肿瘤的肿瘤发热和血液灌注。数值模拟结果表明，表面温度曲线受到提到的性质的影响。但是，归一化温度变化型材不。该方法提供了一种有用的诊断工具，用于估计皮肤肿瘤的热物理性质。

著录项

  • 来源
    《International Journal of Heat and Mass Transfer》 |2021年第12期|121804.1-121804.14|共14页
  • 作者

    Haolong Chen; Kaijie Wang; Zhibo Du; Weiming Liu; Zhanli Liu;

  • 作者单位

    School of Civil Engineering Hefei University of Technology Hefei 230009 China Dept. of Engineering Mechanics Applied Mechanics Lab. Tsinghua University Beijing 100084 China;

    Beijing Tongren Eye Center Beijing Tongren Hospital Beijing Ophthalmology & Visual Sciences Key Lab Capital Medical University Beijing 100730 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beijing Tongren Hospital Beihang University & Capital Medical University Beijing 100730 China;

    Dept. of Engineering Mechanics Applied Mechanics Lab. Tsinghua University Beijing 100084 China;

    Ministry of Education Key Laboratory of Protein Science Collaborative Innovation Center for Biotherapy School of Life Sciences Tsinghua University Beijing 100084 China;

    Dept. of Engineering Mechanics Applied Mechanics Lab. Tsinghua University Beijing 100084 China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Inverse bio-heat conduction problem; Thermophysical properties; Surface temperature; Deep learning; Temperature profiles;

    机译：逆生物导热问题;热物理性质;表面温度;深度学习;温度曲线;

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