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Updated: May 13, 2025

Digital Analysis of Immunostaining of ZW10 Interacting Protein in Human Lung Tissues

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Published on: May 1, 2019

High-performance Data Management for Whole Slide Image Analysis in Digital Pathology.

Haoju Leng¹, Ruining Deng¹, Shunxing Bao²

¹Department of Computer Science, Vanderbilt University, Nashville, TN, USA.

Proceedings of Spie--The International Society for Optical Engineering

|May 9, 2025

Summary

This summary is machine-generated.

The Adaptable IO System version 2 (ADIOS2) significantly accelerates digital pathology whole-slide image analysis by optimizing data access. It achieves a two-fold speed-up in CPU-based tasks and matches high-end GPU performance.

Keywords:

ADIOS2 NVIDIA GDS

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Area of Science:

Digital Pathology
Computational Imaging
Data Management

Background:

Whole-slide imaging (WSI) in digital pathology generates massive datasets.
Patch-level processing of WSIs creates significant input-output (I/O) bottlenecks.
Efficient data access is crucial for timely analysis of gigapixel pathology images.

Purpose of the Study:

To address the I/O challenges in digital pathology whole-slide image analysis.
To implement and evaluate the Adaptable IO System version 2 (ADIOS2) for streamlined data management.
To develop strategies for reducing data retrieval times in pathology workflows.

Main Methods:

Developed a digital pathology-centric pipeline utilizing ADIOS2.
Implemented strategies to optimize data access and retrieval.
Conducted performance evaluations in both CPU-based and GPU-based scenarios.

Main Results:

ADIOS2 achieved a two-fold speed-up in CPU-based image analysis compared to brute-force methods.
In GPU-based deep learning scenarios, ADIOS2 demonstrated performance comparable to NVIDIA Magnum IO GPU Direct Storage (GDS).
This represents an early application of ADIOS2 in the digital pathology domain.

Conclusions:

ADIOS2 effectively mitigates I/O bottlenecks in gigapixel whole-slide image analysis.
The system offers significant performance improvements for both CPU and GPU-based pathology workflows.
The public release of the source code facilitates further research and adoption in digital pathology.