Prior-guided Diffusion Model for Cell Segmentation in Quantitative Phase Imaging

TL;DR

This paper introduces a prior-guided diffusion model for cell segmentation in quantitative phase imaging, significantly improving efficiency and accuracy by incorporating content information into the starting noise, reducing sampling steps.

Contribution

It proposes a novel prior-guided mechanism for diffusion models that uses content information to enhance cell segmentation in QPI with fewer sampling steps.

Findings

Achieved superior segmentation performance with only a single sampling.

Demonstrated the effectiveness of content priors through ablation studies.

Reduced computational inefficiency compared to traditional methods.

Abstract

Purpose: Quantitative phase imaging (QPI) is a label-free technique that provides high-contrast images of tissues and cells without the use of chemicals or dyes. Accurate semantic segmentation of cells in QPI is essential for various biomedical applications. While DM-based segmentation has demonstrated promising results, the requirement for multiple sampling steps reduces efficiency. This study aims to enhance DM-based segmentation by introducing prior-guided content information into the starting noise, thereby minimizing inefficiencies associated with multiple sampling. Approach: A prior-guided mechanism is introduced into DM-based segmentation, replacing randomly sampled starting noise with noise informed by content information. This mechanism utilizes another trained DM and DDIM inversion to incorporate content information from the to-be-segmented images into the starting noise. An evaluation method is also proposed to assess the quality of the starting noise, considering both content and distribution information. Results: Extensive experiments on various QPI datasets for cell segmentation showed that the proposed method achieved superior performance in DM-based segmentation with only a single sampling. Ablation studies and visual analysis further highlighted the significance of content priors in DM-based segmentation. Conclusion: The proposed method effectively leverages prior content information to improve DM-based segmentation, providing accurate results while reducing the need for multiple samplings. The findings emphasize the importance of integrating content priors into DM-based segmentation methods for optimal performance.