Deutsches Krebsforschungszentrum Stiftung Des Offentlichen Rechts

Silvia Seidlitz

Lena Maier-Hein

Sebastian Josef Wirkert

Anant Suraj Vemuri

Leonardo Antonio Ayala Menjivar

Thomas Kirchner

Tim Adler

Disclosed herein is a method of generating 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, 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.

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Heidelberg, Germany

Silvia Seidlitz