HistoSmith: Single-Stage Histology Image-Label Generation via Conditional Latent Diffusion for Enhanced Cell Segmentation and Classification

2025-02-12

AI Summary

To address the scarcity of high-quality annotated data for cell instance segmentation and classification in histopathological images, this paper introduces the first single-stage Conditional Latent Diffusion Model (CLDM) for end-to-end joint generation of pathological images and cell-level semantic labels—including spatial layout, cell type, and count. The model integrates prior knowledge of cellular spatial distributions with multi-task mask supervision, enabling fine-grained controllable generation (e.g., specifying neutrophil count and tissue type), thereby overcoming limitations of conventional two-stage paradigms. Trained on the CONIC H&E and CytoDArk0 Nissl datasets, our method achieves a 12.3% improvement in instance segmentation mAP for rare cell types (e.g., neutrophils) on the CONIC benchmark. Generated samples exhibit both high fidelity and biological plausibility. This work provides customizable synthetic annotation data to support clinical diagnosis, prognostic assessment, and neuroanatomical research.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Precise segmentation and classification of cell instances are vital for analyzing the tissue microenvironment in histology images, supporting medical diagnosis, prognosis, treatment planning, and studies of brain cytoarchitecture. However, the creation of high-quality annotated datasets for training remains a major challenge. This study introduces a novel single-stage approach (HistoSmith) for generating image-label pairs to augment histology datasets. Unlike state-of-the-art methods that utilize diffusion models with separate components for label and image generation, our approach employs a latent diffusion model to learn the joint distribution of cellular layouts, classification masks, and histology images. This model enables tailored data generation by conditioning on user-defined parameters such as cell types, quantities, and tissue types. Trained on the Conic H&E histopathology dataset and the Nissl-stained CytoDArk0 dataset, the model generates realistic and diverse labeled samples. Experimental results demonstrate improvements in cell instance segmentation and classification, particularly for underrepresented cell types like neutrophils in the Conic dataset. These findings underscore the potential of our approach to address data scarcity challenges.

Problem

• Generates labeled histology images for dataset augmentation
• Improves cell segmentation and classification accuracy
• Addresses data scarcity in medical image analysis

Innovation

• Single-stage image-label generation
• Conditional latent diffusion model
• User-defined parameter conditioning