# Co-synthesis of Histopathology Nuclei Image-Label Pairs using a Context-Conditioned Joint Diffusion Model

**Authors:** Seonghui Min, Hyun-Jic Oh, Won-Ki Jeong  
**arXiv:** [2407.14434](https://arxiv.org/abs/2407.14434)  
**Date:** 2024-07-19  
**Summary page:** https://www.summarizepaper.com/en/paper/2407.14434v1/

## Key points

- Scarcity of training data in multi-class histopathology nuclei analysis poses a challenge for learning-based methods
- Previous approaches have used generative models to generate synthetic samples and augment datasets, but lack incorporation of contextual information related to biological tissues
- Existing generative models struggle to produce image-label pairs simultaneously
- A novel framework is introduced for co-synthesizing histopathology nuclei images and paired semantic labels using a context-conditioned joint diffusion model
- The proposed method involves conditioning the diffusion model with nucleus centroid layouts and structure-related text prompts to enhance spatial and structural context information in generation targets
- Granularity of synthesized semantic labels is improved by generating instance-wise nuclei labels using distance maps synthesized concurrently with images and semantic labels
- Main contribution lies in proposing a co-synthesis framework that generates high-quality samples with semantic and instance labels using a single diffusion model
- Approach enables simultaneous generation of complete image-label pairs by modeling joint distribution of histopathology images, semantic labels, and distance maps
- Incorporation of nucleus centroid layouts and text prompts enhances the model's capacity to capture critical visual context details in real histopathology specimens
- Individual nuclei instances in resulting semantic labels are separated using distance maps and conditioned nucleus centroid layouts for high-quality instance labels necessary for advanced nuclei analysis tasks like segmentation algorithms
- Effectiveness demonstrated on diverse datasets from multiple institutions, organs, and modalities through quantitative evaluations and qualitative assessments
- Innovative framework offers comprehensive solution for generating high-quality histopathology images with detailed semantic labels while considering crucial spatial contexts and tissue types.

## Summary

In the field of multi-class histopathology nuclei analysis, the scarcity of training data poses a significant challenge for learning-based methods. To address this issue, previous approaches have utilized generative models to generate synthetic samples and augment the dataset. However, these methods often overlook the importance of incorporating contextual information related to biological tissues, such as shape, spatial layout, and tissue type, into the synthetic data. Additionally, while generative models have shown promise in creating realistic histopathology images, none have been able to simultaneously produce image-label pairs. In this paper, a novel framework is introduced for co-synthesizing histopathology nuclei images and paired semantic labels using a context-conditioned joint diffusion model. The proposed method involves conditioning the diffusion model with nucleus centroid layouts and structure-related text prompts to incorporate spatial and structural context information into the generation targets. Furthermore, the granularity of synthesized semantic labels is enhanced by generating instance-wise nuclei labels using distance maps that are synthesized concurrently with the images and semantic labels. The main contribution of this work lies in proposing a co-synthesis framework for multi-class nuclei datasets that generates high-quality samples with semantic and instance labels using a single diffusion model. By modeling the joint distribution of histopathology images, semantic labels, and distance maps, this approach enables simultaneous generation of complete image-label pairs. Two conditions - nucleus centroid layouts and text prompts - are incorporated to enhance the model's capacity to capture critical visual context details in real histopathology specimens. Moreover, individual nuclei instances in the resulting semantic labels are separated using distance maps and conditioned nucleus centroid layouts to generate high-quality instance labels necessary for advanced nuclei analysis tasks like segmentation algorithms. The effectiveness of this approach is demonstrated on diverse datasets from multiple institutions, organs, and modalities through both quantitative evaluations and qualitative assessments. Overall, this innovative framework offers a comprehensive solution for generating high-quality histopathology images with detailed semantic labels while considering crucial spatial contexts and tissue types. By bridging gaps between existing methodologies and addressing common issues in label synthesis processes, this approach significantly advances the field of multi-class nuclei analysis in histopathology research.