  The analysis of the tumor environment on digital histopathology slides is
becoming key for the understanding of the immune response against cancer,
supporting the development of novel immuno-therapies. We introduce here a novel
deep learning solution to the related problem of tumor epithelium segmentation.
While most existing deep learning segmentation approaches are trained on
time-consuming and costly manual annotation on single stain domain (PD-L1), we
leverage here semi-automatically labeled images from a second stain domain
(Cytokeratin-CK). We introduce an end-to-end trainable network that jointly
segment tumor epithelium on PD-L1 while leveraging unpaired image-to-image
translation between CK and PD-L1, therefore completely bypassing the need for
serial sections or re-staining of slides. Extending the method to differentiate
between PD-L1 positive and negative tumor epithelium regions enables the
automated estimation of the PD-L1 Tumor Cell (TC) score. Quantitative
experimental results demonstrate the accuracy of our approach against
state-of-the-art segmentation methods.
