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Updated: Sep 24, 2025
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Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
Published on: May 3, 2018
8.2K
High-throughput whole-slide scanning to enable large-scale data repository building.
Mark D Zarella1,2, Keysabelis Rivera Alvarez1
1Department of Pathology, Johns Hopkins University, Baltimore, MD, USA.
The Journal of Pathology
|May 5, 2022
Summary
High-throughput slide scanning using whole-slide imaging is crucial for advancing digital pathology and artificial intelligence (AI). Innovations in hardware and informatics enhance efficiency and data quality for large-scale AI development.
Area of Science:
- Digital Pathology
- Artificial Intelligence
- Computational Pathology
Background:
- Digital pathology and AI require efficient digitization of patient tissue slides.
- Large sample sizes and diverse cohorts are essential for robust AI development.
- Current methods must balance efficiency, cost-effectiveness, and data quality.
Purpose of the Study:
- To review practical considerations for deploying high-throughput slide scanning.
- To present strategies for increasing efficiency and maintaining quality in slide digitization.
- To identify challenges and encourage vendor innovation in automation and quality control.
Main Methods:
- Review of technical innovations in whole-slide imaging hardware (scanner capacity, speed, automation).
- Integration of automated informatics approaches with hardware advancements.
- Analysis of practical considerations for high-throughput scanning workflows.
Main Results:
- Hardware and informatics innovations enable efficient workflows and high-quality imaging data.
- Increased scanner capacity, speed, and automation facilitate high-throughput scanning.
- Optimized workflows can reduce personnel requirements while improving data output.
Conclusions:
- High-throughput scanning is key for advancing AI in digital pathology.
- Strategies focusing on efficiency and quality are vital for successful implementation.
- Vendor innovation in automation and quality control is needed to support resource-limited labs.
