Track 23: Whole Slide Imaging (WSI)

sub track:-
High-Resolution Imaging, Digital Archiving and Storage, Remote Access and Telepath ology,  Integration with Artificial Intelligence (AI), Educational and Training Applications, Standardization and Quality Control,  Workflow Efficiency,  Regulatory and Compliance Considerations, Cost Implications, Challenges and Limitations, WholeSlideImaging, WSI, DigitalPathology, PathologyTech, SlideScanning,DigitalImaging, Medical Imaging, Pathology Innovation, HighResolutionImaging, Pathology Automation

Whole Slide Imaging (WSI) refers to the process of scanning entire pathology slides at high resolution to create detailed digital images that can be viewed, analysed, and shared electronically. This technology transforms traditional pathology practices, where pathologists would use a microscope to examine tissue samples on glass slides, by digitizing these slides so they can be examined on a computer screen.
Key Components of Whole Slide Imaging (WSI):

Slide Scanners

High-Resolution Scanning:

Image Quality: WSI systems capture detailed, high-resolution images of entire slides. The resolution typically ranges from 20x to 40x magnification, allowing for detailed examination of tissue samples.

Z-Stacking: Some systems use z-stacking to capture images at multiple focal depths, creating a composite image that provides a complete view of the tissue.

Automation:

Scanning Efficiency: Modern WSI systems are automated to handle multiple slides simultaneously, increasing throughput and efficiency in laboratories.

Digital Image Files

File Formats:

Standard Formats: Digital slides are usually saved in standardized file formats, such as TIFF (Tagged Image File Format) or proprietary formats from specific WSI vendors.

File Size: Due to high resolution, digital slide files can be quite large, requiring substantial storage and management solutions.

Image Analysis

Quantitative Analysis:

Feature Extraction: WSI images can be analyzed to quantify features such as tumor size, cell density, and biomarker expression levels.

Automated Detection: Machine learning and AI algorithms can assist in detecting and classifying various tissue structures and pathological features.

Annotation and Markup: