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Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

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ENHANCED SHARP-GAN FOR HISTOPATHOLOGY IMAGE SYNTHESIS.

Sujata Butte¹, Haotian Wang¹, Aleksandar Vakanski¹

¹University of Idaho, Idaho, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging

|April 4, 2024

Summary

This study introduces a new method for generating realistic histopathology images using nuclei topology and contour regularization. The approach improves synthetic image quality and enhances downstream nuclei segmentation performance.

Keywords:

Histopathology image generation nuclei segmentation

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

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Published on: April 28, 2022

Area of Science:

Digital pathology
Computational imaging
Artificial intelligence in medicine

Background:

Histopathology image synthesis is crucial for addressing data scarcity in deep learning-based cancer detection.
Existing methods often generate unrealistic images with inaccurate nuclei boundaries and artifacts, limiting their practical use.

Purpose of the Study:

To develop a novel approach for enhancing synthetic histopathology image quality.
To improve the accuracy of nuclei boundaries and reduce artifacts in synthesized images.

Main Methods:

The proposed method utilizes nuclei topology and contour regularization for image enhancement.
A skeleton map of nuclei is employed to integrate topology and separate touching nuclei.
Novel contour regularization terms are introduced in the loss function to improve pixel contrast and similarity.

Main Results:

The approach significantly outperforms Sharp-GAN in image quality metrics across two datasets.
Incorporating synthetic images improved nuclei segmentation performance, achieving state-of-the-art results on the TNBC dataset.
Key metrics for nuclei segmentation included detection quality (DQ), segmentation quality (SQ), panoptic quality (PQ), and aggregated Jaccard index (AJI).

Conclusions:

The proposed method effectively enhances synthetic histopathology image quality by addressing nuclei topology and contour irregularities.
The improved synthetic images contribute to achieving state-of-the-art performance in downstream nuclei segmentation tasks.
This work offers a promising solution for data augmentation in computational pathology.