# SiliCoN: Simultaneous Nuclei Segmentation and Color Normalization of Histological Images

arXiv: https://arxiv.org/abs/2506.07028 · HTML: https://mlsanity.com/paper/2506.07028/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 8 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [StainGAN: Stain Style Transfer for Digital Histological Images](https://mlsanity.com/paper/1804.01601/) (2018)
- [Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images](https://mlsanity.com/paper/1810.00236/) (2018)
- [RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans](https://mlsanity.com/paper/1811.01328/) (2018)
- [Nuclei instance segmentation and classification in histopathology images with StarDist](https://mlsanity.com/paper/2203.02284/) (2022)
- [Transformer based multiple instance learning for weakly supervised histopathology image segmentation](https://mlsanity.com/paper/2205.08878/) (2022)
- [Weakly Supervised Nuclei Segmentation via Instance Learning](https://mlsanity.com/paper/2202.01564/) (2022)
- [BoNuS: Boundary Mining for Nuclei Segmentation with Partial Point Labels](https://mlsanity.com/paper/2401.07437/) (2024)

## Abstract (arXiv)

Segmentation of nuclei regions from histological images is an important task for automated computer-aided analysis of histological images, particularly in the presence of impermissible color variation in the color appearance of stained tissue images. While color normalization enables better nuclei segmentation, accurate segmentation of nuclei structures makes color normalization rather trivial. In this respect, the paper proposes a novel deep generative model for simultaneously segmenting nuclei structures and normalizing color appearance of stained histological images.This model judiciously integrates the merits of truncated normal distribution and spatial attention. The model assumes that the latent color appearance information, corresponding to a particular histological image, is independent of respective nuclei segmentation map as well as embedding map information. The disentangled representation makes the model generalizable and adaptable as the modification or loss in color appearance information cannot be able to affect the nuclei segmentation map as well as embedding information. Also, for dealing with the stain overlap of associated histochemical reagents, the prior for latent color appearance code is assumed to be a mixture of truncated normal distributions. The proposed model incorporates the concept of spatial attention for segmentation of nuclei regions from histological images. The performance of the proposed approach, along with a comparative analysis with related state-of-the-art algorithms, has been demonstrated on publicly available standard histological image data sets.