SiliCoN: Simultaneous Nuclei Segmentation and Color Normalization of Histological Images

TL;DR

This paper introduces a deep generative model that simultaneously segments nuclei and normalizes color in histological images, improving accuracy and robustness in automated tissue analysis.

Contribution

It presents a novel model combining truncated normal distribution and spatial attention for joint nuclei segmentation and color normalization, enhancing generalizability and handling stain overlap.

Findings

Outperforms state-of-the-art algorithms on standard datasets.

Effective disentanglement of color appearance and segmentation maps.

Robustness to color variations and stain overlaps.

Abstract

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.