Measurement of blood oxygen saturation (sO₂) by optical imaging oximetry provides invaluable insight into local tissue functions and metabolism. Despite different embodiments and modalities, all label-free optical-imaging oximetry techniques utilize the same principle of sO₂-dependent spectral contrast from haemoglobin. Traditional approaches for quantifying sO₂ often rely on analytical models that are fitted by the spectral measurements. These approaches in practice suffer from uncertainties due to biological variability, tissue geometry, light scattering, systemic spectral bias, and variations in the experimental conditions. Here, we propose a new data-driven approach, termed deep spectral learning (DSL), to achieve oximetry that is highly robust to experimental variations and, more importantly, able to provide uncertainty quantification for each sO₂ prediction. To demonstrate the robustness and generalizability of DSL, we analyse data from two visible light optical coherence tomography (vis-OCT) setups across two separate in vivo experiments on rat retinas. Predictions made by DSL are highly adaptive to experimental variabilities as well as the depth-dependent backscattering spectra. Two neural-network-based models are tested and compared with the traditional least-squares fitting (LSF) method. The DSL-predicted sO₂ shows significantly lower mean-square errors than those of the LSF. For the first time, we have demonstrated en face maps of retinal oximetry along with a pixel-wise confidence assessment. Our DSL overcomes several limitations of traditional approaches and provides a more flexible, robust, and reliable deep learning approach for in vivo non-invasive label-free optical oximetry.

通过光学成像血氧测定法测量血氧饱和度 ( s O _{2 ) 可以为了解局部组织功能和新陈代谢提供宝贵的见解。}尽管实施例和模式不同，但所有无标记光学成像血氧测定技术都利用来自血红蛋白的s O ₂依赖性光谱对比的相同原理。量化s O ₂的传统方法通常依赖于由光谱测量拟合的分析模型。这些方法在实践中由于生物变异性、组织几何形状、光散射、系统光谱偏差和实验条件的变化而受到不确定性的影响。在这里，我们提出了一种新的数据驱动方法，称为深度光谱学习（DSL），以实现对实验变化高度稳健的血氧测定，更重要的是，能够为每个 s O 2 预测提供不确定性_量化。为了证明 DSL 的稳健性和普遍性，我们分析了大鼠视网膜上两个独立的体内实验中两个可见光光学相干断层扫描 (vis-OCT) 设置的数据。DSL 做出的预测高度适应实验变化以及与深度相关的后向散射光谱。对两种基于神经网络的模型进行了测试，并与传统的最小二乘拟合（LSF）方法进行了比较。DSL 预测的s O _{2 的}均方误差明显低于 LSF 的均方误差。我们首次展示了视网膜血氧饱和度的面部图以及逐像素置信度评估。我们的 DSL 克服了传统方法的一些局限性，为体内非侵入式无标记光血氧测定提供了更灵活、稳健和可靠的深度学习方法。