Fluorescence Image Histology Pattern Transformation using Image Style Transfer
Mohammadhassan Izadyyazdanabadi, Evgenii Belykh, Xiaochun Zhao,, Leandro Borba Moreira, Sirin Gandhi, Claudio Cavallo, Jennifer Eschbacher,, Peter Nakaji, Mark C. Preul, and Yezhou Yang

TL;DR
This paper introduces a neural style transfer method to enhance confocal laser endomicroscopy images of brain tumors, making them more interpretable and similar to traditional stained histology slides for better diagnosis.
Contribution
The study applies neural style transfer to CLE images, improving their quality and interpretability by mimicking H&E stained histology, enabling real-time cellular-level tissue examination.
Findings
84 out of 100 transformed images had fewer artifacts.
Transformed images showed more noticeable critical structures.
Style transfer improved interpretability and diagnostic potential.
Abstract
Confocal laser endomicroscopy (CLE) allow on-the-fly in vivo intraoperative imaging in a discreet field of view, especially for brain tumors, rather than extracting tissue for examination ex vivo with conventional light microscopy. Fluorescein sodium-driven CLE imaging is more interactive, rapid, and portable than conventional hematoxylin and eosin (H&E)-staining. However, it has several limitations: CLE images may be contaminated with artifacts (motion, red blood cells, noise), and neuropathologists are mainly trained on colorful stained histology slides like H&E while the CLE images are gray. To improve the diagnostic quality of CLE, we used a micrograph of an H&E slide from a glioma tumor biopsy and image style transfer, a neural network method for integrating the content and style of two images. This was done through minimizing the deviation of the target image from both the content…
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Taxonomy
TopicsAI in cancer detection · Cell Image Analysis Techniques · Radiomics and Machine Learning in Medical Imaging
