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

arXiv: https://arxiv.org/abs/2208.08284 · HTML: https://mlsanity.com/paper/2208.08284/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 2 references that are in the map.
- First posted on arXiv 16 Aug 2022.

## Builds on (most cited first, 1 of 1)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)

## Built on by (most cited first, 1 of 1)

- [Digital staining in optical microscopy using deep learning -- a review](https://mlsanity.com/paper/2303.08140/) (2023)

## Abstract (arXiv)

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.