Automatic identification and delineation of germ layer components in H&E stained images of teratomas derived from human and nonhuman primate embryonic stem cells

  • Authors:

  • Ramamurthy Bhagavatula;Matthew Fickus;W. Kelly;Chenlei Guo;John A. Ozolek;Carlos A. Castro;Jelena Kovačević

  • Affiliations:

  • Dept. of ECE, Carnegie Mellon University, Pittsburgh, PA and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA;Dept. of Mathematics and Statistics, Air Force Institute of Technology, Wright-Patterson Air Force Base, OH;Dept. of ECE, Carnegie Mellon University, Pittsburgh, PA;Dept. of ECE, Carnegie Mellon University, Pittsburgh, PA;Dept. of Pathology, Children's Hospital of Pittsburgh, University of Pittsburgh, PA;Dept. Obstetrics and Gynecology, Magee-Womens Research Institute and Foundation, University of Pittsburgh, PA;Dept. of BME, Carnegie Mellon University, Pittsburgh, PA and Dept. of ECE, Carnegie Mellon University, Pittsburgh, PA and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, P ...

  • Venue:
  • ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
  • Year:
  • 2010

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Abstract

We present a methodology for the automatic identification and delineation of germ-layer components in H&E stained images of teratomas derived from human and nonhuman primate embryonic stem cells. A knowledge and understanding of the biology of these cells may lead to advances in tissue regeneration and repair, the treatment of genetic and developmental syndromes, and drug testing and discovery. As a teratoma is a chaotic organization of tissues derived from the three primary embryonic germ layers, H&E teratoma images often present multiple tissues, each of having complex and unpredictable positions, shapes, and appearance with respect to each individual tissue as well as with respect to other tissues. While visual identification of these tissues is timeconsuming, it is surprisingly accurate, indicating that there exist enough visual cues to accomplish the task. We propose automatic identification and delineation of these tissues by mimicking these visual cues. We use pixel-based classification, resulting in an encouraging range of classification accuracies from 74.9% to 93.2% for 2- to 5-tissue classification experiments at diffferent scales.