用深度学习提升术中组织氧合实时监测精度
Clinical Validation of Deep Learning for Real-Time Tissue Oxygenation Estimation Using Spectral Imaging
- 用蒙特卡洛模拟光谱训练神经网络，实现无接触氧合估计
- 模型与毛细血管乳酸值相关性高于传统线性解混方法
- 对抗域自适应有效缩小模拟与真实数据差距，适合临床应用
准确、实时的组织缺血监测对评估组织健康和指导手术至关重要。光谱成像在无需接触的情况下实现术中组织氧合监测方面具有巨大潜力。由于难以获得直接参考氧合值，传统方法依赖于线性解混技术，但这类方法易受假设限制，且线性关系在实际中未必成立。本文提出基于蒙特卡洛模拟光谱的深度学习方法，训练全连接网络（FCN）和卷积神经网络（CNN），并引入域对抗训练策略以弥合模拟数据与真实临床光谱数据之间的差异。结果表明，这些深度学习模型在手术期间光谱成像中与毛细血管乳酸值（缺氧的公认标志物）的相关性优于传统线性解混方法。值得注意的是，域对抗训练有效降低了域间差距，提升了真实临床环境下的性能。
原文摘要 · Abstract (English)
Accurate, real-time monitoring of tissue ischemia is crucial to understand tissue health and guide surgery. Spectral imaging shows great potential for contactless and intraoperative monitoring of tissue oxygenation. Due to the difficulty of obtaining direct reference oxygenation values, conventional methods are based on linear unmixing techniques. These are prone to assumptions and these linear relations may not always hold in practice. In this work, we present deep learning approaches for real-time tissue oxygenation estimation using Monte-Carlo simulated spectra. We train a fully connected neural network (FCN) and a convolutional neural network (CNN) for this task and propose a domain-adversarial training approach to bridge the gap between simulated and real clinical spectral data. Results demonstrate that these deep learning models achieve a higher correlation with capillary lactate measurements, a well-known marker of hypoxia, obtained during spectral imaging in surgery, compared to traditional linear unmixing. Notably, domain-adversarial training effectively reduces the domain gap, optimizing performance in real clinical settings.
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