1. Quantitative analysis of histological tissue image based on cytological profiles and spatial statistics
- Author
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Jason Link, Joe W. Gray, Young Hwan Chang, Guillaume Thibault, Brett Johnson, Vahid Azimi, Danielle M. Jorgens, and Adam A. Margolin
- Subjects
0301 basic medicine ,Tissue architecture ,Computer science ,H&E stain ,03 medical and health sciences ,medicine ,Humans ,Computer vision ,Hematoxylin ,Spatial analysis ,Cell Nucleus ,Spatial Analysis ,Staining and Labeling ,business.industry ,Cancer ,Pattern recognition ,Image segmentation ,medicine.disease ,Spectral clustering ,Cell nucleus ,030104 developmental biology ,medicine.anatomical_structure ,Tissue sections ,Eosine Yellowish-(YS) ,Artificial intelligence ,business ,Quantitative analysis (chemistry) ,Image based - 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.
- Published
- 2016