用深度学习直接从光声信号估算血氧饱和度并分割血管，无需复杂光照建模。
Deep Learning-Driven Quantitative Spectroscopic Photoacoustic Imaging for Segmentation and Oxygen Saturation Estimation
- 设计混合神经网络（Hybrid-Net）联合预测血氧饱和度与血管分割。
- 实验中血氧误差低至0.003，分割准确率达0.998。
- 适用于需要高精度血氧成像的临床研究，如乳腺组织检测。
光谱光声（sPA）成像有望无创实现体内血氧饱和度（sO2）估算。然而，量化准确需精确的光通量估计，而异质组织中不同波长光的吸收与散射差异大，建模困难。本文提出深度神经网络 Hybrid-Net，可同时估计血管内 sO2 并分割血管。仅对 Hybrid-Net 分割出的血管区域优化 sO2 误差，提升预测准确性。网络先在三维蒙特卡洛模拟的乳腺组织光传输数据（700 nm 与 850 nm）上训练，再在含血池的仿生组织模型实验数据（同波长）上微调。定量评估显示：仿真中分割准确率 ≥ 0.978（噪声 0dB–35dB），实验中达 0.998；仿真中平均 sO2 均方误差 ≤ 0.048，实验中低至 0.003。结果表明，Hybrid-Net 可在不估计光通量的前提下实现精准血氧成像，推动 in-vivo sO2 估算发展。
原文摘要 · Abstract (English)
Spectroscopic photoacoustic (sPA) imaging can potentially estimate blood oxygenation saturation (sO2) in vivo noninvasively. However, quantitatively accurate results require accurate optical fluence estimates. Robust modeling in heterogeneous tissue, where light with different wavelengths can experience significantly different absorption and scattering, is difficult. In this work, we developed a deep neural network (Hybrid-Net) for sPA imaging to simultaneously estimate sO2 in blood vessels and segment those vessels from surrounding background tissue. sO2 error was minimized only in blood vessels segmented in Hybrid-Net, resulting in more accurate predictions. Hybrid-Net was first trained on simulated sPA data (at 700 nm and 850 nm) representing initial pressure distributions from three-dimensional Monte Carlo simulations of light transport in breast tissue. Then, for experimental verification, the network was retrained on experimental sPA data (at 700 nm and 850 nm) acquired from simple tissue mimicking phantoms with an embedded blood pool. Quantitative measures were used to evaluate Hybrid-Net performance with an averaged segmentation accuracy of >= 0.978 in simulations with varying noise levels (0dB-35dB) and 0.998 in the experiment, and an averaged sO2 mean squared error of <= 0.048 in simulations with varying noise levels (0dB-35dB) and 0.003 in the experiment. Overall, these results show that Hybrid-Net can provide accurate blood oxygenation without estimating the optical fluence, and this study could lead to improvements in in-vivo sO2 estimation.
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