Diffusion-based Virtual Staining from Polarimetric Mueller Matrix Imaging
This addresses the need for improved virtual staining tools to help pathologists interpret polarization-based imaging results, representing an incremental advancement in a nascent field.
The paper tackles the problem of converting polarimetric Mueller Matrix images to standardized stained images for pathology diagnosis by proposing a Regulated Bridge Diffusion Model (RBDM), which outperforms benchmark methods on a manually collected dataset of 18,000 paired images.
Polarization, as a new optical imaging tool, has been explored to assist in the diagnosis of pathology. Moreover, converting the polarimetric Mueller Matrix (MM) to standardized stained images becomes a promising approach to help pathologists interpret the results. However, existing methods for polarization-based virtual staining are still in the early stage, and the diffusion-based model, which has shown great potential in enhancing the fidelity of the generated images, has not been studied yet. In this paper, a Regulated Bridge Diffusion Model (RBDM) for polarization-based virtual staining is proposed. RBDM utilizes the bidirectional bridge diffusion process to learn the mapping from polarization images to other modalities such as H\&E and fluorescence. And to demonstrate the effectiveness of our model, we conduct the experiment on our manually collected dataset, which consists of 18,000 paired polarization, fluorescence and H\&E images, due to the unavailability of the public dataset. The experiment results show that our model greatly outperforms other benchmark methods. Our dataset and code will be released upon acceptance.