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Related Concept Videos

Immunocytochemistry and Immunohistochemistry01:22

Immunocytochemistry and Immunohistochemistry

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Immunocytochemistry (ICC) and immunohistochemistry (IHC) are techniques that use antibodies to check for specific proteins or antigens in a sample. The technique was first published by Albert Coons in 1941 to detect the presence of pneumococcal antigen in tissue sections from mice infected with Pneumococcus. Immunocytochemistry helps localization of proteins or antigens in individual cells like blood cells, stem cells, etc., while immunohistochemistry does the same for tissue samples.
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Supervised Information Mining From Weakly Paired Images for Breast IHC Virtual Staining.

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    This study introduces a novel generative adversarial network for virtual immunohistochemistry (IHC) staining from Hematoxylin and Eosin (H&E) images. The method enhances breast cancer diagnosis by accurately simulating IHC results, overcoming limitations of traditional staining methods.

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    Area of Science:

    • Digital pathology
    • Computational imaging
    • Biomedical image analysis

    Background:

    • Immunohistochemistry (IHC) is crucial for breast cancer diagnosis and treatment planning but is complex and costly.
    • Hematoxylin and Eosin (H&E) staining is a more accessible alternative, but lacks IHC's specific diagnostic information.
    • Virtual staining from H&E to IHC could bridge this gap, but accurate image registration and supervision are challenging.

    Purpose of the Study:

    • To develop a virtual staining method to generate IHC images from H&E images for breast cancer.
    • To address the challenge of inaccurate pixel-level pairing between adjacent H&E and IHC tissue layers.
    • To improve the accuracy and applicability of virtual IHC staining in clinical settings.

    Main Methods:

    • Proposing a generative adversarial network (GAN) incorporating Optimal Transport-based Supervised Information Mining (OT-SIM) for instance-level supervision.
    • Implementing Pathological Correlation-based Supervised Information Mining (PC-SIM) for batch-level supervision, leveraging image correlations.
    • Utilizing adjacent layer tissue images for training, despite imperfect pixel-level alignment.

    Main Results:

    • The proposed SIM-GAN method demonstrated superior performance in virtual IHC staining compared to existing state-of-the-art techniques.
    • Quantitative and qualitative evaluations on two benchmark breast tissue datasets confirmed the method's effectiveness.
    • The approach successfully mined supervised information from H&E and IHC image pairs, even with registration inaccuracies.

    Conclusions:

    • The developed virtual staining technique offers a promising solution to reduce the cost and complexity associated with traditional IHC staining.
    • SIM-GAN effectively addresses the challenge of non-ideal image pairing by employing novel information mining mechanisms.
    • This advancement holds potential for improving personalized treatment strategies in breast cancer through more accessible IHC-like information.