Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors
Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors
复制标题
用于脑肿瘤DCE-MRI分析的全自动深度学习驱动系统
DOI：
10.1016/j.artmed.2019.101769
复制
发表时间：
2020-01-01
影响因子：
7.5
通讯作者：
Hayball, Michael P.
中科院分区：
文献类型：
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作者：
Nalepa, Jakub;Lorenzo, Pablo Ribalta;Hayball, Michael P.
动态对比增强磁共振成像（DCE-MRI）在脑肿瘤的诊断和分级中具有重要作用。尽管人工DCE生物标志物提取算法通过提供肿瘤预后和预测的定量信息来提高DCE- mri的诊断率，但它们耗时且容易出现人为错误。在本文中，我们提出了一个全自动的端到端DCE-MRI脑肿瘤分析系统。我们的深度学习驱动技术不需要任何用户交互，它产生可重复的结果，并根据基准和临床数据进行严格验证。此外，我们还引入了用于药代动力学建模的血管输入函数的三次模型，与目前的状态相比，该模型显著降低了拟合误差，同时还引入了用于确定血管输入区域的实时算法。一项广泛的实验研究和统计测试表明，我们的系统在使用单个GPU处理整个输入DCE-MRI研究所需时间不到3分钟的情况下，提供了最先进的结果。
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumors. Although manual DCE biomarker extraction algorithms boost the diagnostic yield of DCE-MRI by providing quantitative information on tumor prognosis and prediction, they are time-consuming and prone to human errors. In this paper, we propose a fully-automated, end-to-end system for DCE-MRI analysis of brain tumors. Our deep learning-powered technique does not require any user interaction, it yields reproducible results, and it is rigorously validated against benchmark and clinical data. Also, we introduce a cubic model of the vascular input function used for pharmacokinetic modeling which significantly decreases the fitting error when compared with the state of the art, alongside a real-time algorithm for determination of the vascular input region. An extensive experimental study, backed up with statistical tests, showed that our system delivers state-of-the-art results while requiring less than 3 min to process an entire input DCE-MRI study using a single GPU.