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Related Concept Videos

Overview of Microscopy Techniques01:22

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The early pioneers of microscopy opened a window into the invisible world of microorganisms. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes that leveraged nonvisible light, such as fluorescence microscopy that uses an ultraviolet light source and electron microscopy that uses short-wavelength electron beams. These advances significantly improved magnification, image resolution, and contrast. By comparison, the...
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Related Experiment Video

Updated: Sep 24, 2025

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
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A low-cost pathological image digitalization method based on 5 times magnification scanning.

Kai Sun1, Yanhua Gao2, Ting Xie1

  • 1Department of Biomedical Engineering, School of Basic Medical Science, Central South University, Changsha, China.

Quantitative Imaging in Medicine and Surgery
|May 3, 2022
PubMed
Summary

This study introduces a superresolution method to create high-resolution whole slide images from low-resolution scans, significantly cutting digital pathology costs. Pathologists confirmed the generated images are suitable for diagnosis, offering a viable digitalization alternative.

Keywords:
Digital pathologylow costlow-resolution (LR) scanningsuperresolution (SR)

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Area of Science:

  • Digital pathology and medical imaging.
  • Artificial intelligence in healthcare.
  • Image processing and computer vision.

Background:

  • Digital pathology relies on high-magnification whole slide images (WSIs).
  • High-resolution WSIs result in large file sizes (1-5 GB), increasing storage and time costs.
  • Current digitalization methods face significant storage and network exchange challenges.

Purpose of the Study:

  • To develop a superresolution (SR) method for restoring image details in digital pathology.
  • To reduce the storage and time costs associated with high-resolution WSI digitalization.
  • To provide a cost-effective and efficient alternative for digital pathology workflows.

Main Methods:

  • A superresolution (SR) strategy using low-resolution (LR) 5× scans.
  • Implementation of a multiscale generative adversarial network (GAN).
  • Sequential generation of 10×, 20×, and 40× high-resolution (HR) images from LR images.
  • Training and testing on a dataset of 100,000 pathological images across 10 human body systems.

Main Results:

  • Generated 10×, 20×, and 40× images from 5× WSIs, reducing file size by up to 1/64.
  • Achieved superior peak-signal-to-noise ratios (PSNR) and structural similarity (SSIM) compared to other SR methods.
  • Pathologist evaluations confirmed generated images contain sufficient diagnostic information (average visual scoring 3.63-3.74).
  • High consistency between diagnoses of generated and real images (Kappa test value 0.990).

Conclusions:

  • The proposed SR method effectively generates high-quality diagnostic images from low-resolution scans.
  • Significant reduction in time and storage costs for digital pathology.
  • Demonstrates potential for clinical application as an alternative digitalization method.