A High-Resolution Tile-Based Approach for Classifying Biological Regions in Whole-Slide Histopathological Images

  • R. A. Hoffman
  • S. Kothari
  • J. H. Phan
  • May D. Wang
Part of the IFMBE Proceedings book series (IFMBE, volume 42)

Abstract

Computational analysis of histopathological whole slide images (WSIs) has emerged as a potential means for improving cancer diagnosis and prognosis. However, an open issue relating to the automated processing of WSIs is the identification of biological regions such as tumor, stroma, and necrotic tissue on the slide. We develop a method for classifying WSI portions (512×512-pixel tiles) into biological regions by (1) extracting a set of 461 image features from each WSI tile, (2) optimizing tile-level prediction models using nested cross-validation on a small (600 tile) manually annotated tilelevel training set, and (3) validating the models against a much larger (1.7×106 tile) data set for which ground truth was available on the whole-slide level. We calculated the predicted prevalence of each tissue region and compared this prevalence to the ground truth prevalence for each image in an independent validation set. Results show significant correlation between the predicted (using automated system) and reported biological region prevalences with p<0.001 for eight of nine cases considered.

Keywords

Whole Slide Images Histopathology Clinical Decision Support Systems 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • R. A. Hoffman
    • 1
  • S. Kothari
    • 2
  • J. H. Phan
    • 1
  • May D. Wang
    • 1
    • 2
    • 3
  1. 1.Department of Biomedical EngineeringGeorgia Institute of Technology and Emory UniversityAtlantaUSA
  2. 2.Department of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaUSA
  3. 3.Winship Cancer Institute, Parker H. Petit Institute of Bioengineering and Biosciences, Institute of People and TechnologyGeorgia Institute of Technology and Emory UniversityAtlantaUSA

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