# Image Translation Based Nuclei Segmentation for Immunohistochemistry Images

arXiv: https://arxiv.org/abs/2208.06202 · HTML: https://mlsanity.com/paper/2208.06202/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 12 Aug 2022.

## Builds on (most cited first, 9 of 9)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation](https://mlsanity.com/paper/1907.10830/) (2019)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [Unsupervised Object Segmentation by Redrawing](https://mlsanity.com/paper/1905.13539/) (2019)
- [Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue](https://mlsanity.com/paper/1803.11293/) (2018)
- [CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images](https://mlsanity.com/paper/2101.00442/) (2021)
- [Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue](https://mlsanity.com/paper/2001.07267/) (2020)
- [GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis](https://mlsanity.com/paper/1901.04059/) (2019)

## Built on by (most cited first, 3 of 3)

- [Digital staining in optical microscopy using deep learning -- a review](https://mlsanity.com/paper/2303.08140/) (2023)
- [BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology](https://mlsanity.com/paper/2503.20880/) (2025)
- [Unpaired Image-to-Image Translation for Segmentation and Signal Unmixing](https://mlsanity.com/paper/2505.20746/) (2025)

## Abstract (arXiv)

Numerous deep learning based methods have been developed for nuclei segmentation for H&E images and have achieved close to human performance. However, direct application of such methods to another modality of images, such as Immunohistochemistry (IHC) images, may not achieve satisfactory performance. Thus, we developed a Generative Adversarial Network (GAN) based approach to translate an IHC image to an H&E image while preserving nuclei location and morphology and then apply pre-trained nuclei segmentation models to the virtual H&E image. We demonstrated that the proposed methods work better than several baseline methods including direct application of state of the art nuclei segmentation methods such as Cellpose and HoVer-Net, trained on H&E and a generative method, DeepLIIF, using two public IHC image datasets.