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.

通过引入先验内容信息来提高基于马尔可夫随机场的分割方法，优化细胞分割效果。

基于先验引导的扩散模型用于定量相位成像中的细胞分割

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

Quantitative phase imaging (QPI) is a label-free technique that provides
optical path length information for transparent specimens, finding utility in
biology, materials science, and engineering. Here, we present quantitative
phase imaging of a 3D stack of phase-only objects using a
wavelength-multiplexed diffractive optical processor. Utilizing multiple
spatially engineered diffractive layers trained through deep learning, this
diffractive processor can transform the phase distributions of multiple 2D
objects at various axial positions into intensity patterns, each encoded at a
unique wavelength channel. These wavelength-multiplexed patterns are projected
onto a single field-of-view (FOV) at the output plane of the diffractive
processor, enabling the capture of quantitative phase distributions of input
objects located at different axial planes using an intensity-only image sensor.
Based on numerical simulations, we show that our diffractive processor could
simultaneously achieve all-optical quantitative phase imaging across several
distinct axial planes at the input by scanning the illumination wavelength. A
proof-of-concept experiment with a 3D-fabricated diffractive processor further
validated our approach, showcasing successful imaging of two distinct phase
objects at different axial positions by scanning the illumination wavelength in
the terahertz spectrum. Diffractive network-based multiplane QPI designs can
open up new avenues for compact on-chip phase imaging and sensing devices.

基于深度学习训练的多程面量化相位成像设计可为紧凑型芯片相位成像和传感器设备开辟新的途径。