High-performance data management for whole slide image analysis in digital pathology

Haoju Leng; Ruining Deng; Shunxing Bao; Dazheng Fang; Bryan A. Millis; Yucheng Tang; Haichun Yang; Xiao Wang; Yifan Peng; Lipeng Wan; Yuankai Huo

doi:10.1117/12.3006273

3 April 2024 High-performance data management for whole slide image analysis in digital pathology

Haoju Leng, Ruining Deng, Shunxing Bao, Dazheng Fang, Bryan A. Millis, Yucheng Tang, Haichun Yang, Xiao Wang, Yifan Peng, Lipeng Wan, Yuankai Huo

Author Affiliations +

Proceedings Volume 12933, Medical Imaging 2024: Digital and Computational Pathology; 129330Y (2024) https://doi.org/10.1117/12.3006273
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

When dealing with giga-pixel digital pathology in whole-slide imaging, a notable proportion of data records holds relevance during each analysis operation. For instance, when deploying an image analysis algorithm on whole-slide images (WSI), the computational bottleneck often lies in the input-output (I/O) system. This is particularly notable as patch-level processing introduces a considerable I/O load onto the computer system. However, this data management process could be further paralleled, given the typical independence of patch-level image processes across different patches. This paper details our endeavors in tackling this data access challenge by implementing the Adaptable IO System version 2 (ADIOS2). Our focus has been constructing and releasing a digital pathology-centric pipeline using ADIOS2, which facilitates streamlined data management across WSIs. Additionally, we’ve developed strategies aimed at curtailing data retrieval times. The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario (“CPU scenario”), and (2) a GPU-based deep learning framework scenario (“GPU scenario”). Our findings reveal noteworthy outcomes. Under the CPU scenario, ADIOS2 showcases an impressive two-fold speed-up compared to the brute-force approach. In the GPU scenario, its performance stands on par with the cutting-edge GPU I/O acceleration framework, NVIDIA Magnum IO GPU Direct Storage (GDS). From what we know, this appears to be among the initial instances, if any, of utilizing ADIOS2 within the field of digital pathology. The source code has been made publicly available at https://github.com/hrlblab/adios.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haoju Leng, Ruining Deng, Shunxing Bao, Dazheng Fang, Bryan A. Millis, Yucheng Tang, Haichun Yang, Xiao Wang, Yifan Peng, Lipeng Wan, and Yuankai Huo "High-performance data management for whole slide image analysis in digital pathology", Proc. SPIE 12933, Medical Imaging 2024: Digital and Computational Pathology, 129330Y (3 April 2024); https://doi.org/10.1117/12.3006273

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