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Fixation and Sectioning

Fixation and Sectioning

Two basic types of preparation are used to visualize specimens with a light microscope: wet mounts and fixed specimens.
The simplest type of preparation is the wet mount, in which the specimen is placed in a drop of liquid on the slide. A liquid specimen can be directly deposited on the slide using a dropper. Solid specimens, such as skin scraping, can be placed on the slide before adding a drop of liquid to prepare the wet mount. Sometimes the liquid is simply water, but stains are often added...

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High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

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Unsupervised Multi-Domain Progressive Stain Transfer Guided by Style Encoding Dictionary.

Xianchao Guan, Yifeng Wang, Yiyang Lin

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 10, 2024

Summary

This summary is machine-generated.

GramGAN enables unsupervised multi-stain transfer in histopathology, reducing computational costs. This deep learning method accurately converts between staining types, improving glomeruli segmentation performance.

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Area of Science:

Histopathology
Computational Pathology
Digital Pathology

Background:

Histopathology relies on H&E and special stains for tissue visualization.
Deep learning offers virtual staining to save time and labor.
Current methods require training separate models for each stain pair, increasing resource demands.

Purpose of the Study:

To develop an unsupervised, multi-domain stain transfer method for histopathology.
To address the computational inefficiency of existing stain transfer techniques.
To improve the accuracy of glomeruli segmentation using virtual staining.

Main Methods:

Proposed GramGAN, an unsupervised multi-domain stain transfer method.
Utilized cascaded Style-Guided blocks for progressive stain transfer.
Designed a style encoding dictionary to capture staining characteristics.
Implemented a Rényi entropy-based regularization term for style discrimination.

Main Results:

GramGAN achieved accurate and efficient transfer among multiple staining styles.
The method demonstrated superior performance compared to existing approaches.
Transferred H&E images to PAS and PASM stains significantly improved glomeruli detection and segmentation accuracy.
A new special stained image dataset for glomeruli segmentation was created and published.

Conclusions:

GramGAN offers an effective solution for unsupervised multi-stain transfer in digital pathology.
The method reduces computational costs and improves the efficiency of virtual staining.
This approach enhances diagnostic capabilities by improving image analysis for tasks like glomeruli segmentation.