Quantitative analysis of histological tissue image based on cytological profiles and spatial statistics

Young Hwan Chang, Guillaume Thibault, Vahid Azimi, Brett Johnson, Danielle Jorgens, Jason Link, Adam Margolin, Joe Gray

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

The cellular heterogeneity and complex tissue architecture of most tumor samples is a major obstacle in image analysis on standard hematoxylin and eosin-stained (H&E) tissue sections. A mixture of cancer and normal cells complicates the interpretation of their cytological profiles. Furthermore, spatial arrangement and architectural organization of cells are generally not reflected in cellular characteristics analysis. To address these challenges, first we describe an automatic nuclei segmentation of H&E tissue sections. In the task of deconvoluting cellular heterogeneity, we adopt Landmark based Spectral Clustering (LSC) to group individual nuclei in such a way that nuclei in the same group are more similar. We next devise spatial statistics for analyzing spatial arrangement and organization, which are not detectable by individual cellular characteristics. Our quantitative, spatial statistics analysis could benefit H&E section analysis by refining and complementing cellular characteristics analysis.

Original languageEnglish (US)
Title of host publication2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1175-1178
Number of pages4
Volume2016-October
ISBN (Electronic)9781457702204
DOIs
Publication statusPublished - Oct 13 2016
Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States
Duration: Aug 16 2016Aug 20 2016

Other

Other38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
CountryUnited States
CityOrlando
Period8/16/168/20/16

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ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

Chang, Y. H., Thibault, G., Azimi, V., Johnson, B., Jorgens, D., Link, J., ... Gray, J. (2016). Quantitative analysis of histological tissue image based on cytological profiles and spatial statistics. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 (Vol. 2016-October, pp. 1175-1178). [7590914] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2016.7590914