Multispectral snapshot imaging of skin microcirculatory hemoglobin oxygen saturation using artificial neural networks trained on in vivo data

Significance: Developing algorithms for estimating blood oxygenation from snapshot multi-spectral imaging (MSI) data is challenging due to the complexity of sensor characteristics and photon transport modeling in tissue. We circumvent this using a method where artificial neural networks (ANNs) are trained on in vivo MSI data with target values from a point-measuring reference method. Aim: To develop and evaluate a methodology where a snapshot filter mosaic camera is utilized for imaging skin hemoglobin oxygen saturation (S-O2), using ANNs. Approach: MSI data were acquired during occlusion provocations. ANNs were trained to estimate S-O2 with MSI data as input, targeting data from a validated probe-based reference system. Performance of ANNs with different properties and training data sets was compared. Results: The method enables spatially resolved estimation of skin tissue S-O2. Results are comparable to those acquired using a Monte-Carlo-based approach when relevant training data are used. Conclusions: Training an ANN on in vivo MSI data covering a wide range of target values acquired during an occlusion protocol enable real-time estimation of S-O2 maps. Data from the probe-based reference system can be used as target despite differences in sampling depth and measurement position. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License.Funding Agencies|VINNOVA Grants via the Swelife and MedTech4Health programsVinnova [2016-02211, 2017-01435, 2019-01522]