Novel Deep Learning Approach to Derive Cytokeratin Expression and Epithelium Segmentation from DAPI
This incremental approach addresses the limited marker capacity in multiplexed immunofluorescence panels for tumor microenvironment analysis, potentially aiding patient selection for immunotherapies.
The paper tackles the problem of synthesizing cytokeratin (CK) staining from DAPI images in non-small cell lung cancer to segment epithelial regions, achieving results comparable to expert annotations on stained CK.
Generative Adversarial Networks (GANs) are state of the art for image synthesis. Here, we present dapi2ck, a novel GAN-based approach to synthesize cytokeratin (CK) staining from immunofluorescent (IF) DAPI staining of nuclei in non-small cell lung cancer (NSCLC) images. We use the synthetic CK to segment epithelial regions, which, compared to expert annotations, yield equally good results as segmentation on stained CK. Considering the limited number of markers in a multiplexed IF (mIF) panel, our approach allows to replace CK by another marker addressing the complexity of the tumor micro-environment (TME) to facilitate patient selection for immunotherapies. In contrast to stained CK, dapi2ck does not suffer from issues like unspecific CK staining or loss of tumoral CK expression.