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Creating Digital Ready Slides - A Practical Guide

Olga Colgan
Olga Colgan Global Brand Strategy Director, Leica Biosystems

The adoption of digital pathology is a multifaceted project involving many stakeholders across the pathology department. The impact on the laboratory is not isolated to simply installing a scanner but rather affects the whole workflow to generate optimized Digital Ready Slides. Standardization of histological slide preparation requires focusing on optimizing individual workflow steps and a holistic overview of the complete process from sample acquisition right through to diagnosis. Knowing this in advance and taking appropriate steps to support effectively change management can promote engagement and pave a path to success.

Key Learning Objectives:

  • Define the critical attributes of Digital Ready Slides
  • Demonstrate the impact of tissue preparation steps on scan quality
  • Provide practical guidance to creating Digital Ready Slides and additional considerations at each laboratory step

Download Creating Digital Ready Slides - A Practical Guide!


발표자 소개

Olga Colgan
Olga Colgan , Global Brand Strategy Director, Leica Biosystems

Dr. Colgan has over a decade of experience in the digital pathology sector and is focused on how this new and disruptive technology can be leveraged to provide real benefits in both the healthcare and research domains. Prior to working with Leica Biosystems, she came from a research background with a BSc in Biotechnology and a PhD in Vascular Biology from Dublin City University, Ireland.

참조 문헌

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  4. College of American Pathologists, “COVID-19 – Remote Sign-Out Guidance” (2020). Available at: capatholo.gy/3ghf4Wu.
  5. Centers for Medicare & Medicaid Services, “Clinical Laboratory Improvement Amendments (CLIA) Laboratory Guidance During COVID-19 Public Health Emergency” (2020). Available at: go.cms.gov/2LGD2wh.
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  7. McCarthy, J. & Minsky, M.L. & Rochester, N. & Shannon, C.E.. (1955).proposal for the Dartmouth summer research project on artificial intelligence. Artificial Intelligence: Critical Concepts. 2. 44-53.
  8. Kanavati, Fahdi & Toyokawa, Gouji & Momosaki, Seiya & Rambeau, Michael & Kozuma, Yuka & Shoji, Fumihiro & Yamazaki, Koji & Takeo, Sadanori & Iizuka, Osamu & Tsuneki, Masayuki. (2020). Weakly-supervised learning for lung carcinoma classification using deep learning. Scientific Reports. 10.10.1038/s41598-020-66333-x.
  9. Iizuka, Osamu & Kanavati, Fahdi & Kato, Kei & Rambeau, Michael & Arihiro, Koji & Tsuneki, Masayuki. (2020). Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours. Scientific Reports. 10. 10.1038/s41598-020-58467-9.
  10. McCampbell, Adrienne & Raghunathan, Varun & Tom-Moy, May & Workman, Richard & Haven, Rick & Ben-Dor, Amir & Rasmussen, Ole & Jacobsen, Lars & Lindberg, Martin & Yamada, N. & Schembri, Carol. (2017). Tissue Thickness Effects on Immunohistochemical Staining Intensity of Markers of Cancer. Applied Immunohistochemistry & Molecular Morphology. 27. 1. 10.1097/PAI.0000000000000593.
  11. Masuda, Shinobu & Suzuki, Ryohei & Kitano, Yuriko & Nishimaki, Haruna & Kobayashi, Hiroko & Nakanishi, Yoko & Yokoi, Hideo. (2020). Tissue Thickness Interferes With the Estimation of the Immunohistochemical Intensity: Introduction of a Control System for Managing Tissue Thickness. Applied Immunohistochemistry & Molecular Morphology. Publish Ahead of Print. 1. 10.1097/PAI.0000000000000859.
  12. Arif, Ahmed & Stuerzlinger, Wolfgang. (2009). Analysis of text entry performance metrics. TIC-STH’09: 2009 IEEE Toronto International Conference - Science and Technology for Humanity. 100 - 105. 10.1109/TICSTH.2009.5444533.

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