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Updated: May 15, 2025

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Optimizing Storage and Computational Efficiency: An Efficient Algorithm for Whole Slide Image Size Reduction.

Shahriar Faghani¹, D Chamil Codipilly², Mana Moassefi¹

¹Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, Rochester, MN.

Mayo Clinic Proceedings. Digital Health

|April 10, 2025

Summary

This summary is machine-generated.

A new algorithm efficiently reduces whole slide imaging (WSI) file sizes by removing background, aiding storage and analysis. This WSI compression maintains image resolution and deep learning model performance for dysplasia detection.

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Area of Science:

Digital Pathology
Computational Imaging
Bioinformatics

Background:

Whole slide imaging (WSI) generates large files, posing challenges for storage, transfer, and analysis.
Efficient processing of WSI is crucial for advancing digital pathology workflows and research.

Purpose of the Study:

To develop and validate an image-processing algorithm for reducing WSI file size.
To assess the impact of WSI size reduction on the performance of deep learning models for dysplasia detection.

Main Methods:

The algorithm converts color WSIs to grayscale, binarizes images, and processes foreground masks.
Connected components are identified, and bounding boxes are calculated to extract tissue-containing regions.
A rectangle-packer package is used to find the smallest bounding rectangle enclosing all tissue components.

Main Results:

The algorithm achieved a mean WSI size reduction of 7.11×.
Deep learning model performance for Barrett esophagus dysplasia grading on reduced-size WSIs was comparable to original WSIs.

Conclusions:

The developed algorithm offers an effective solution for WSI size reduction.
This method facilitates efficient storage, transfer, and analysis of WSIs in research and clinical settings.