  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.
