Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Zhuchen Shao, Mark A. Anastasio, Hua Li

TL;DR

This work tackles the inefficiency of diffusion-model based cell segmentation in quantitative phase imaging by introducing prior-guided starting noise. It presents a two-LDM framework (LDM-P for content priors and LDM-S for segmentation) with DDIM inversion to convert input images into content-informed noise, enabling accurate single-sample segmentation. Extensive experiments on HeLa and CHO QPI datasets show that PG-DiffSeg achieves state-of-the-art or competitive results with a single sampling, outperforming traditional deterministic methods and many multi-sample diffusion baselines. Ablation studies corroborate the value of content priors and DDIM inversion for preserving structure while maintaining Gaussian-like noise, highlighting practical gains in speed and accuracy for label-free cell segmentation.

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.

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

TL;DR

This work tackles the inefficiency of diffusion-model based cell segmentation in quantitative phase imaging by introducing prior-guided starting noise. It presents a two-LDM framework (LDM-P for content priors and LDM-S for segmentation) with DDIM inversion to convert input images into content-informed noise, enabling accurate single-sample segmentation. Extensive experiments on HeLa and CHO QPI datasets show that PG-DiffSeg achieves state-of-the-art or competitive results with a single sampling, outperforming traditional deterministic methods and many multi-sample diffusion baselines. Ablation studies corroborate the value of content priors and DDIM inversion for preserving structure while maintaining Gaussian-like noise, highlighting practical gains in speed and accuracy for label-free cell segmentation.

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.
Paper Structure (23 sections, 6 equations, 3 figures, 4 tables, 1 algorithm)

This paper contains 23 sections, 6 equations, 3 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Diffusion model
  4. DDIM sampling and its inversion process
  5. Proposed framework
  6. Overview of the proposed method
  7. Latent diffusion model for prior extraction (LDM-P)
  8. Latent diffusion model for segmentation (LDM-S)
  9. Numerical Studies
  10. Datasets
  11. Model architecture
  12. Implementation details
  13. Comparison methods
  14. Deterministic segmentation methods
  15. DM-based segmentation methods
  16. ...and 8 more sections

Figures (3)

  • Figure 1: Proposed prior-guided DM-based segmentation (PG-DiffSeg) method. (a) The process of prior-guided DM-based segmentation, (b) The process of prior information extraction. Pre-trained LDM-P was employed to generate content-informed starting noise directly from a given image through DDIM inversion process. This obtained noise, informed by the image's content and following a Gaussian distribution, will be input to the LDM-S for segmentation.
  • Figure 2: Visualization analysis. Visualization results are presented for all methods across two single annotation tasks. The dashed block highlights that these results are specific to the random sampling-based method. Live cells are represented in red, while dead cells are shown in orange. The black square highlights areas where the proposed method performs better than other methods.
  • Figure 3: Visualization analysis for the content prior. The canny edge detector is utilized to obtain contour information, while histograms are employed to analyze distribution information. The red box highlights the specified area.