Novel Deep Learning Approach to Derive Cytokeratin Expression and Epithelium Segmentation from DAPI
Year: 2022
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Segerer, Felix Jakob, Nekolla, Katharina, Rognoni, Lorenz, Kapil, Ansh, Schick, Markus, Angell, Helen, Schmidt, Günter
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4298317679