Estimation of tissue oxygen saturation from RGB images and sparse hyperspectral signals based on conditional generative adversarial network.

Purpose: Intra-operative measurement of tissue oxygen saturation ([Formula: see text]) is important in detection of ischaemia, monitoring perfusion and identifying disease. Hyperspectral imaging (HSI) measures the optical reflectance spectrum of the tissue and uses this information to quantify its composition, including [Formula: see text]. However, real-time monitoring is difficult due to capture rate and data processing time.

Methods: An endoscopic system based on a multi-fibre probe was previously developed to sparsely capture HSI data (sHSI). These were combined with RGB images, via a deep neural network, to generate high-resolution hypercubes and calculate [Formula: see text]. To improve accuracy and processing speed, we propose a dual-input conditional generative adversarial network, Dual2StO2, to directly estimate [Formula: see text] by fusing features from both RGB and sHSI.

Results: Validation experiments were carried out on in vivo porcine bowel data, where the ground truth [Formula: see text] was generated from the HSI camera. Performance was also compared to our previous super-spectral-resolution network, SSRNet in terms of mean [Formula: see text] prediction accuracy and structural similarity metrics. Dual2StO2 was also tested using simulated probe data with varying fibre number.

Conclusions: [Formula: see text] estimation by Dual2StO2 is visually closer to ground truth in general structure and achieves higher prediction accuracy and faster processing speed than SSRNet. Simulations showed that results improved when a greater number of fibres are used in the probe. Future work will include refinement of the network architecture, hardware optimization based on simulation results, and evaluation of the technique in clinical applications beyond [Formula: see text] estimation.