HistoSmith: Single-Stage Histology Image-Label Generation via Conditional Latent Diffusion for Enhanced Cell Segmentation and Classification
Journal:
arXiv
Published Date:
Feb 12, 2025
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.
