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.
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Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Image Synthesis
Secondary Subject Area: Application: Histopathology
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