Harnessing non-destructive 3D pathology

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作者
Jonathan T. C. Liu
Adam K. Glaser
Kaustav Bera
Lawrence D. True
Nicholas P. Reder
Kevin W. Eliceiri
Anant Madabhushi
机构
[1] University of Washington,Department of Mechanical Engineering
[2] University of Washington,Department of Laboratory Medicine and Pathology
[3] University of Washington,Department of Bioengineering
[4] Case Western Reserve University,Department of Biomedical Engineering
[5] University of Wisconsin,Department of Medical Physics
[6] University of Wisconsin,Department of Biomedical Engineering
[7] Morgridge Institute for Research,undefined
[8] Louis Stokes Cleveland Veterans Administration Medical Center,undefined
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High-throughput methods for slide-free three-dimensional (3D) pathological analyses of whole biopsies and surgical specimens offer the promise of modernizing traditional histology workflows and delivering improvements in diagnostic performance. Advanced optical methods now enable the interrogation of orders of magnitude more tissue than previously possible, where volumetric imaging allows for enhanced quantitative analyses of cell distributions and tissue structures that are prognostic and predictive. Non-destructive imaging processes can simplify laboratory workflows, potentially reducing costs, and can ensure that samples are available for subsequent molecular assays. However, the large size of the feature-rich datasets that they generate poses challenges for data management and computer-aided analysis. In this Perspective, we provide an overview of the imaging technologies that enable 3D pathology, and the computational tools—machine learning, in particular—for image processing and interpretation. We also discuss the integration of various other diagnostic modalities with 3D pathology, along with the challenges and opportunities for clinical adoption and regulatory approval.
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页码:203 / 218
页数:15
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