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Acceleration of Two Point Correlation Function Calculation for Pathology Image Segmentation.

Lee A D Cooper1, Joel H Saltz1, Umit Catalyurek2

  • 1Center for Comprehensive Informatics, Emory University, Atlanta, GA.

Proceedings. IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology
|July 17, 2018
PubMed
Summary
This summary is machine-generated.

Accelerating the calculation of two-point correlation functions (TPCF) is crucial for analyzing large pathology images. This study introduces efficient computational methods, including distributed and GPU parallelization, to overcome TPCF analysis challenges in digital pathology.

Keywords:
Digital PathologyGraphical Processing UnitImage SegmentationMicroscopyTwo Point Correlation Function

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Area of Science:

  • Digital Pathology
  • Computational Imaging
  • Biomedical Image Analysis

Background:

  • High-resolution microscopy generates gigapixel pathology images, posing challenges for tissue region segmentation.
  • Textural analysis of digitized pathology sections is essential but computationally intensive.
  • The two-point correlation function (TPCF) is an effective textural feature for tissue analysis.

Purpose of the Study:

  • To present and evaluate methods for accelerating the deterministic calculation of point correlation functions.
  • To address the computational burden of TPCF calculations in gigapixel pathology images.
  • To improve the efficiency of TPCF analysis for digital pathology applications.

Main Methods:

  • Utilized theoretical approaches to reduce computational load for TPCF calculation.
  • Implemented parallelization on distributed systems (64 compute nodes).
  • Developed parallelization strategies for graphics processors (GPU) on a single desktop machine.

Main Results:

  • The correlation updating method demonstrated an 8-35x speedup over frequency domain methods.
  • Distributed computation on 64 nodes yielded an additional 42x speedup.
  • GPU parallelization provided a further 11-16x speedup, enabling desktop use.

Conclusions:

  • The presented methods significantly accelerate TPCF calculation for large-scale pathology images.
  • Distributed and GPU parallelization offer practical solutions for intractable TPCF computations.
  • These advancements facilitate more efficient textural analysis in digital pathology.