Diffusion-based Virtual Staining from Polarimetric Mueller Matrix Imaging

2025-03-03

AI Summary

Label-free, low-cost virtual staining remains challenging in digital pathology due to structural distortions and semantic inconsistencies in cross-modal mapping. Method: This study proposes a diffusion-driven virtual staining framework based on polarimetric Mueller matrix imaging. To address these challenges, we introduce the Regulated Bridge Diffusion Model (RBDM)—the first controlled bridging diffusion mechanism tailored for polarization-based virtual staining—enabling bidirectional bridge modeling for high-fidelity translation from Mueller matrix images to H&E or fluorescence-stained counterparts. Contribution/Results: Evaluated on a large-scale, self-collected dataset of 18,000 paired polarimetric–histologically stained images, our method significantly outperforms existing baselines. Pathologists consistently rate it highly for tissue structural fidelity, nuclear detail restoration, and pathological semantic consistency. The approach establishes a new, interpretable, and highly reliable paradigm for label-free digital pathology.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Content-based information diffusion
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

Abstract

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.

Problem

• Develops a diffusion-based model for virtual staining from polarimetric images.
• Converts polarimetric Mueller Matrix images to standardized stained images.
• Addresses the lack of public datasets by using a manually collected dataset.

Innovation

• Regulated Bridge Diffusion Model for virtual staining
• Bidirectional bridge diffusion enhances image fidelity
• Converts polarimetric images to H&E and fluorescence