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Harnessing non-destructive 3D pathology.

Jonathan T C Liu1,2,3, Adam K Glaser4, Kaustav Bera5

  • 1Department of Mechanical Engineering, University of Washington, Seattle, WA, USA. jonliu@uw.edu.

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|February 16, 2021
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Summary
This summary is machine-generated.

Three-dimensional (3D) pathology uses advanced imaging to analyze whole tissue samples, improving diagnostics. Computational tools like machine learning aid interpretation, but data management remains a challenge.

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Area of Science:

  • Pathology
  • Medical Imaging
  • Computational Biology

Background:

  • Traditional histology workflows are being modernized by high-throughput, slide-free three-dimensional (3D) pathological analyses.
  • Advanced optical methods enable interrogation of significantly larger tissue volumes than previously possible.
  • Volumetric imaging facilitates enhanced quantitative analyses of cell distributions and tissue structures for prognostic and predictive insights.

Purpose of the Study:

  • To provide an overview of imaging technologies enabling 3D pathology.
  • To discuss computational tools, particularly machine learning, for image processing and interpretation in 3D pathology.
  • To examine the integration of 3D pathology with other diagnostic modalities and explore challenges for clinical adoption.

Main Methods:

  • Overview of high-throughput, slide-free imaging technologies for whole biopsies and surgical specimens.
  • Discussion of computational tools, including machine learning, for processing and interpreting large, feature-rich 3D datasets.
  • Exploration of integrating 3D pathology with other diagnostic modalities.

Main Results:

  • High-throughput 3D pathology offers modernized workflows and improved diagnostic performance.
  • Non-destructive imaging simplifies laboratory processes, potentially reduces costs, and preserves samples for molecular assays.
  • Large datasets generated by 3D pathology present challenges in data management and computer-aided analysis.

Conclusions:

  • 3D pathology, powered by advanced imaging and computational tools, promises to revolutionize diagnostics.
  • Addressing data management and computational challenges is crucial for clinical adoption.
  • Integration with other modalities and regulatory considerations are key for the future of 3D pathology.