Novel Deep Learning Approach to Derive Cytokeratin Expression and Epithelium Segmentation from DAPI

Felix Jakob Segerer, Katharina Nekolla, Lorenz Rognoni, Ansh Kapil, Markus Schick, Helen Angell, Günter Schmidt

Keywords: Digital Pathology, GANs, Semantic Segmentation, Immunofluorescence

TL;DR: Applying a novel deep learning approach we derive cytokeratin expression and epithelium segmentation from immunofluorescent DAPI staining of pathology slides.

Abstract: 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.

Registration: I acknowledge that acceptance of this work at MIDL requires at least one of the authors to register and present the work during the conference.

Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.

Paper Type: novel methodological ideas without extensive validation

Primary Subject Area: Image Synthesis

Secondary Subject Area: Application: Histopathology

Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.