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HistoClean: Open-source software for histological image pre-processing and augmentation to improve development of

Kris D McCombe1, Stephanie G Craig1, Amélie Viratham Pulsawatdi1

  • 1Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland.

Computational and Structural Biotechnology Journal
|September 15, 2021
PubMed
Summary

HistoClean is a new, user-friendly graphical tool that simplifies digital pathology image pre-processing and augmentation for deep learning models. It improves convolutional neural network accuracy for cancer prediction tasks without requiring coding knowledge.

Keywords:
AI, Artificial IntelligenceAUC, Area Under CurveArtificial intelligenceDIA, Digital Image AnalysisDigital image analysisGUI, Graphical User InterfaceHistoCleanImage augmentationImage pre-processingOpen-source softwareROC, Receiver-Operator Characteristic

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Area of Science:

  • Digital Pathology
  • Computational Pathology
  • Artificial Intelligence in Medicine

Background:

  • Digital pathology has advanced cancer research, driving the use of deep learning (DL) for image analysis.
  • Image pre-processing and augmentation are crucial for robust DL models, but often require coding expertise.
  • A gap exists between pathology and computer science, hindering the adoption of advanced image analysis techniques.

Purpose of the Study:

  • To introduce HistoClean, a graphical user interface (GUI) for streamlined image pre-processing and augmentation in digital pathology.
  • To bridge the knowledge gap between pathologists, biomedical scientists, and computer scientists.
  • To demonstrate the utility of HistoClean in improving DL model performance for cancer-related tasks.

Main Methods:

  • Developed HistoClean, a user-friendly GUI integrating multiple image processing modules.
  • Utilized HistoClean for pre-processing digital pathology images for a convolutional neural network (CNN).
  • Applied classical image augmentation and pre-processing techniques through HistoClean.

Main Results:

  • HistoClean successfully pre-processed images, enhancing a simple CNN model's accuracy.
  • Improvements in model accuracy were observed at tile, region of interest, and patient levels.
  • The study confirmed HistoClean's effectiveness in improving standard DL workflows.

Conclusions:

  • HistoClean provides an accessible solution for image pre-processing and augmentation in digital pathology.
  • The tool facilitates the application of advanced techniques without prior coding knowledge, benefiting interdisciplinary collaboration.
  • HistoClean is a valuable, free, and open-source resource for the digital pathology and AI research community.