In the realm of histopathological examinations, which are crucial for disease diagnosis and rely on histochemical staining, traditional staining methods have long been hampered by issues like color interference and high costs. This has spurred the exploration of virtual staining, a computer-aided technique that transfers staining styles. However, virtual staining has faced significant roadblocks, namely, the stringent need for structural consistency and the arduous task of separating content and style due to the scarcity of pixel-level paired data.

We have now introduced a groundbreaking solution: a dual-path inversion virtual staining method integrated with prompt learning. Their Dual Path Prompted Strategy is a key innovation. By leveraging a feature adapter function in the Style Target Path, it generates reference images for inversion. Simultaneously, it utilizes the input image inversion as the Structural Target Path, where visual prompt images play a vital role in maintaining structural integrity while preserving style details. Additionally, the StainPrompt Optimization mechanism refines the process. It optimizes the null visual prompt as an "operator" for dual-path inversion around pivotal noise at each time step, ensuring highly accurate reconstruction.

The research underwent rigorous experimental validation. The ANHIR dataset was utilized, and multiple evaluation metrics, including SSIM, CSS, MS-SSIM, FID, and PSNR, were employed. When compared with various baseline methods, such as GAN-based techniques and those relying on pre-trained diffusion models, the new method demonstrated remarkable competitiveness across multiple metrics, with a particularly strong performance in maintaining structural consistency. Ablation studies further verified the effectiveness of each component of the proposed method, highlighting its robustness and reliability.

This novel training-free unpaired multi-domain stain transfer method, based on single pre-trained diffusion models, represents a significant leap forward. It not only resolves the long-standing structural consistency issue in virtual staining but also paves the way for the advancement of digital pathology, holding great promise for improving disease diagnosis and research in the future.

The overall framework of the proposed dual path inversion method.

The code is available at:  github.com/DianaNerualNetwork/StainPromptInversion

Paper is available in : https://arxiv.org/abs/2412.11106