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Related Concept Videos

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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Related Experiment Video

Updated: Jun 16, 2026

An In Vitro 3D Model and Computational Pipeline to Quantify the Vasculogenic Potential of iPSC-Derived Endothelial Progenitors
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Iris: A Next Generation Digital Pathology Rendering Engine.

Ryan Erik Landvater1, Ulysses Balis1

  • 1University of Michigan Medical School, Department of Pathology, 2800 Plymouth Road, Ann Arbor, MI 48109-2800, USA.

Journal of Pathology Informatics
|January 20, 2025
PubMed
Summary
This summary is machine-generated.

Iris Core is a new digital pathology rendering system that significantly improves whole slide imaging speed and quality. It achieves high-performance rendering, overcoming adoption barriers and enhancing the digital pathology experience.

Keywords:
Digital pathologyDigital scope render enginePerformance digital pathologyTechnologies for improved whole slide imagingTime field of viewTime per tileVulkan

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Area of Science:

  • Digital pathology
  • Computational imaging
  • Medical technology

Background:

  • Digital pathology adoption is hindered by slow, low-quality image rendering compared to traditional glass slides.
  • Whole slide imaging (WSI) offers advantages but faces usability and performance challenges.
  • Existing systems struggle to match the visual fidelity and speed of conventional microscopy.

Purpose of the Study:

  • Introduce Iris Core, a novel high-performance rendering engine for digital pathology.
  • Detail the performance metrics and system architecture of Iris Core.
  • Demonstrate Iris Core's capability to overcome current digital pathology rendering limitations.

Main Methods:

  • Developed Iris Core using C++ and Vulkan, a low-level GPU API.
  • Implemented novel rapid tile buffering algorithms for efficient image processing.
  • Designed a system architecture with stateless process isolation and explicit GPU synchronization.

Main Results:

  • Iris Core achieves sustained 120 FPS slide rendering across platforms.
  • Buffers new slide fields of view in 10ms (standard) and 30ms (enhanced detail).
  • Processes low-power cytology at 100-160μs per tile, with a median buffering rate of 1.36 GB/s.

Conclusions:

  • Iris Core significantly surpasses existing digital pathology rendering performance benchmarks.
  • The system demonstrates exceptional speed, detail, and scalability for WSI.
  • Iris Core is poised to enhance digital pathology adoption and usability.