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Multi-objective Parameter Auto-tuning for Tissue Image Segmentation Workflows.

Luis F R Taveira1, Tahsin Kurc2,3, Alba C M A Melo1

  • 1Department of Computer Science, University of Brasília, Brasília, Brazil.

Journal of Digital Imaging
|November 8, 2018
PubMed
Summary

Automating parameter tuning for nucleus segmentation software significantly improves accuracy and reduces processing time. This new platform enhances disease biomarker analysis in large tissue image datasets.

Keywords:
CancerCell morphologyComputer-assisted image analysisDigital pathologyParameter auto-tuning

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Area of Science:

  • Computational pathology
  • Bioimage analysis
  • Medical image processing

Background:

  • Accurate nucleus segmentation is crucial for extracting biomarkers in tissue images, impacting disease prognosis.
  • Manual parameter tuning for segmentation workflows is time-consuming and computationally intensive.
  • Existing nucleus segmentation pipelines require optimization for robust performance on large datasets.

Purpose of the Study:

  • To develop and present a software platform for automated multi-objective parameter tuning in tissue image segmentation.
  • To enhance the accuracy and efficiency of nucleus/cell segmentation pipelines.
  • To facilitate robust image analysis for large-scale biomedical datasets.

Main Methods:

  • A software platform integrating multi-objective parameter auto-tuning for nucleus segmentation workflows.
  • Optimization methods for efficient parameter space searching.
  • High-performance computing integration for accelerated parameter tuning.
  • Deployment via Docker container with a 3D Slicer interface extension.

Main Results:

  • Improved segmentation quality by an average of 1.20-1.29× across three workflows.
  • Reduced segmentation workflow execution time by up to 11.79×.
  • Efficiently searched a vast parameter space (billions to trillions of points) using a small sample (approx. 100 points).
  • Demonstrated effective use of parallel systems to accelerate tuning and segmentation.

Conclusions:

  • The proposed software platform automates parameter tuning, enhancing nucleus segmentation accuracy and efficiency.
  • The solution significantly reduces computational cost and time for image analysis in pathology.
  • The platform's integration with 3D Slicer and Docker ensures ease of deployment and use in research settings.