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Related Experiment Video

Updated: May 3, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
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HCTTI: High-Performance Heterogeneous Computing Toolkit for Tissue Image Stain Normalization.

Yan Jiang1,2,3, Bo Wang4, Weipeng Xing5

  • 1School of Software, Quanzhou University of Information Engineering, Quanzhou, Fujian, 362000, China. jianghnu@hnu.edu.cn.

Journal of Imaging Informatics in Medicine
|January 17, 2025
PubMed
Summary

A new toolkit, HCTTI, significantly accelerates whole slide imaging (WSI) analysis for deep learning in cancer diagnosis. It optimizes WSI reading, normalization, and saving, enabling faster and more efficient medical diagnostics.

Keywords:
Digital pathologyDistributed computingHistopathology image analysisOpen source softwareStain normalization

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Area of Science:

  • Digital pathology
  • Computational medicine
  • Medical image analysis

Background:

  • Whole slide imaging (WSI) is crucial for cancer diagnosis and treatment, enabling advanced medical diagnostics through deep learning.
  • Color and intensity variations in WSI across different institutions hinder deep learning classification accuracy.
  • Current WSI processing and normalization heavily rely on CPUs, resulting in suboptimal computational performance.

Purpose of the Study:

  • To introduce a High-Performance Heterogeneous Computing Toolkit for Tissue Image (HCTTI) designed to optimize WSI analysis.
  • To evaluate the performance of various WSI readers, color normalization techniques, and tile serialization formats.
  • To demonstrate the efficiency gains of HCTTI in distributed and multi-node GPU environments.

Main Methods:

  • Developed HCTTI, integrating system-level optimizations for WSI reading, tile normalization, and tile saving.
  • Compared HCTTI's performance against existing tools like OpenSlide and TIAToolbox for WSI reading and normalization.
  • Assessed the efficiency of different serialization formats (HDF5, PNG, Zarr) for storing normalized tissue image tiles.
  • Implemented multi-node distributed GPU processing for enhanced scalability.

Main Results:

  • HCTTI achieved a 7x speedup in WSI reading compared to OpenSlide.
  • GPU-accelerated Macenko normalization within HCTTI was 9x faster than TIAToolbox implementation.
  • HDF5 demonstrated superior performance for storing normalized images, offering 13x faster writing and 2x faster reading than PNG.
  • A 13x speedup was observed for normalizing a single WSI using HCTTI compared to TIAToolbox.
  • Linear acceleration was achieved with the multi-node distributed GPU implementation.

Conclusions:

  • HCTTI provides a comprehensive solution for distributed WSI reading, normalization, and serialization, significantly improving processing efficiency.
  • The toolkit's performance enhancements have the potential to accelerate deep learning-based WSI analysis for medical diagnosis.
  • HCTTI's optimizations can contribute to more effective and efficient cancer diagnosis and treatment strategies.