A computer-implemented method of generating one or more augmented images of tissue of a patient undergoing open treatment, in particular open surgery, wherein each augmented image associates at least one tissue parameter with a region or pixel of the image of the tissue, said method comprising the following steps: estimating a spectral composition of light illuminating a region of interest of the tissue, obtaining one or more multispectral images of the region of interest, characterized by applying a machine learning based regressor or classifier to the one or more multispectral images, or an image derived from said multispectral image, to thereby derive one or more tissue parameters associated with image regions or pixels of the corresponding multispectral image, wherein said regressor or classifier has been trained to predict the one or more tissue parameters from a multispectral image under a given spectral composition of illumination, wherein the regressor or classifier employed is made to match the estimated spectral composition of light illuminating said region of interest of the tissue by one of - selecting the regressor or classifier among a plurality regressors or classifiers that have been previously trained for different training illuminations, such that the spectral composition of the training illumination is the most similar to the estimated spectral composition of said light illuminating said region of interest, or - transforming the obtained multispectral image based on information derived from the estimated spectral composition of the light illuminating said region of interest and applying a standard regressor or classifier to the transformed multispectral image, which has been trained under a standard illumination, wherein the transformation is capable of compensating a change in the multispectral image due to a deviation in the spectral composition of the illumination from the standard illumination, or - retraining an already trained regressor or classifier using simulation data that is adapted to the estimated spectral composition of light illuminating said region of interest. estimating a spectral composition of light illuminating a region of interest of the tissue, obtaining one or more multispectral images of the region of interest, characterized by applying a machine learning based regressor or classifier to the one or more multispectral images, or an image derived from said multispectral image, to thereby derive one or more tissue parameters associated with image regions or pixels of the corresponding multispectral image, wherein said regressor or classifier has been trained to predict the one or more tissue parameters from a multispectral image under a given spectral composition of illumination, wherein the regressor or classifier employed is made to match the estimated spectral composition of light illuminating said region of interest of the tissue by one of - selecting the regressor or classifier among a plurality regressors or classifiers that have been previously trained for different training illuminations, such that the spectral composition of the training illumination is the most similar to the estimated spectral composition of said light illuminating said region of interest, or - transforming the obtained multispectral image based on information derived from the estimated spectral composition of the light illuminating said region of interest and applying a standard regressor or classifier to the transformed multispectral image, which has been trained under a standard illumination, wherein the transformation is capable of compensating a change in the multispectral image due to a deviation in the spectral composition of the illumination from the standard illumination, or - retraining an already trained regressor or classifier using simulation data that is adapted to the estimated spectral composition of light illuminating said region of interest. - selecting the regressor or classifier among a plurality regressors or classifiers that have been previously trained for different training illuminations, such that the spectral composition of the training illumination is the most similar to the estimated spectral composition of said light illuminating said region of interest, or - transforming the obtained multispectral image based on information derived from the estimated spectral composition of the light illuminating said region of interest and applying a standard regressor or classifier to the transformed multispectral image, which has been trained under a standard illumination, wherein the transformation is capable of compensating a change in the multispectral image due to a deviation in the spectral composition of the illumination from the standard illumination, or - retraining an already trained regressor or classifier using simulation data that is adapted to the estimated spectral composition of light illuminating said region of interest.