Automatic evaluation of traumatic brain injury based on terahertz imaging with machine learning
Automatic evaluation of traumatic brain injury based on terahertz imaging with machine learning
复制标题
基于太赫兹成像和机器学习的脑外伤自动评估
DOI：
10.1364/oe.26.006371
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发表时间：
2018-03-05
期刊：
影响因子：
3.8
通讯作者：
Yao, Jianquan
中科院分区：
文献类型：
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作者：
Shi, Jia;Wang, Yuye;Yao, Jianquan
不同程度创伤性脑损伤（TBI）的影像学诊断和预后对早期护理和临床治疗具有重要意义。特别是，轻度脑外伤的准确识别是目前神经外科无标签成像技术的瓶颈。在这里，我们报告了一种基于机器学习（ML）的太赫兹（THz）连续波（CW）传输成像识别TBI的自动评估方法。提出了一种结合空间域透光率分布特征和归一化灰度直方图统计分布特征的生物太赫兹图像特征提取新方法。在提取的特征库基础上，通过特征选择和参数优化对不同程度的脑损伤进行ML算法分类。分类准确率最高可达87.5%。受试者工作特征（ROC）曲线下面积（AUC）得分均大于0.9，说明该评价方法具有较好的泛化能力。此外，通过不同的方法参数和诊断标准，分析了该系统在轻度TBI识别中的优异性能。该系统可扩展到多种疾病，将成为生物医学自动诊断的有力工具。(C) 2018年美国光学学会
The imaging diagnosis and prognostication of different degrees of traumatic brain injury (TBI) is very important for early care and clinical treatment. Especially, the exact recognition of mild TBI is the bottleneck for current label-free imaging technologies in neurosurgery. Here, we report an automatic evaluation method for TBI recognition with terahertz (THz) continuous-wave (CW) transmission imaging based on machine learning (ML). We propose a new feature extraction method for biological THz images combined with the transmittance distribution features in spatial domain and statistical distribution features in normalized gray histogram. Based on the extracted feature database, ML algorithms are performed for the classification of different degrees of TBI by feature selection and parameter optimization. The highest classification accuracy is up to 87.5%. The area under the curve (AUC) scores of the receiver operating characteristics (ROC) curve are all higher than 0.9, which shows this evaluation method has a good generalization ability. Furthermore, the excellent performance of the proposed system in the recognition of mild TBI is analyzed by different methodological parameters and diagnostic criteria. The system can be extensible to various diseases and will be a powerful tool in automatic biomedical diagnostics. (C) 2018 Optical Society of America