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PST-Diff: Achieving High-Consistency Stain Transfer by Diffusion Models With Pathological and Structural Constraints
Abstract:
Histopathological examinations heavily rely on hematoxylin and eosin (HE) and immunohistochemistry (IHC) staining. IHC staining can offer more accurate diagnostic details but it brings significant financial and time costs. Furthermore, either re-staining HE-stained slides or using adjacent slides for IHC may compromise the accuracy of pathological diagnosis due to information loss. To address these challenges, we develop PST-Diff, a method for generating virtual IHC images from HE images based on diffusion models, which allows pathologists to simultaneously view multiple staining results from the same tissue slide. To maintain the pathological consistency of the stain transfer, we propose the asymmetric attention mechanism (AAM) and latent transfer (LT) module in PST-Diff. Specifically, the AAM can retain more local pathological information of the source domain images, while ensuring the model's flexibility in generating virtual stained images that highly confirm to the target domain. Subsequently, the LT module transfers the implicit representations across different domains, effectively alleviating the bias introduced by direct connection and further enhancing the pathological consistency of PST-Diff. Furthermore, to maintain the structural consistency of the stain transfer, the conditional frequency guidance (CFG) module is proposed to precisely control image generation and preserve structural details according to the frequency recovery process. To conclude, the pathological and structural consistency constraints provide PST-Diff with effectiveness and superior generalization in generating stable and functionally pathological IHC images with the best evaluation score. In general, PST-Diff offers prospective application in clinical virtual staining and pathological image analysis.
Insights
This study introduces PST-Diff, a novel method using diffusion models to create virtual immunohistochemistry (IHC) images from hematoxylin and eosin (HE) stained slides. This innovation aims to reduce costs and improve diagnostic accuracy in histopathology.
Area of Science:
- Digital Pathology
- Computational Imaging
- Artificial Intelligence in Medicine
Background:
- Histopathological diagnosis relies on Hematoxylin and Eosin (HE) and Immunohistochemistry (IHC) staining.
- IHC provides detailed diagnostic information but incurs high costs and time.
- Staining adjacent slides or re-staining HE slides for IHC can lead to information loss and reduced diagnostic accuracy.
Purpose of the Study:
- To develop PST-Diff, a method for generating virtual IHC images from HE images using diffusion models.
- To enable simultaneous viewing of multiple staining results from a single tissue slide.
- To address the limitations of traditional staining methods in terms of cost, time, and accuracy.
Main Methods:
- Development of PST-Diff, a diffusion model-based method for virtual IHC image generation.
- Incorporation of an asymmetric attention mechanism (AAM) to preserve local pathological information and ensure target domain adherence.
- Integration of a latent transfer (LT) module to transfer implicit representations and reduce domain bias.
- Implementation of a conditional frequency guidance (CFG) module to maintain structural consistency and control image generation.
Main Results:
- PST-Diff effectively generates virtual IHC images from HE images.
- The method maintains pathological consistency through AAM and LT modules.
- Structural consistency is preserved using the CFG module.
- PST-Diff demonstrates superior generalization and stable, functionally pathological image generation with top evaluation scores.
Conclusions:
- PST-Diff offers a cost-effective and efficient solution for virtual staining in histopathology.
- The method enhances diagnostic accuracy by allowing multiple virtual stains from a single slide.
- PST-Diff shows significant potential for clinical virtual staining and pathological image analysis.
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