AI Article Synopsis
- The study highlights the challenges posed by cellular diversity and complex tissue structures in analyzing H&E-stained tumor samples, making it difficult to differentiate between cancerous and normal cells.
- To tackle these issues, the authors introduce an automatic nuclei segmentation method along with Landmark based Spectral Clustering (LSC) to effectively group similar nuclei.
- They also propose new spatial statistics for better understanding the arrangement and organization of cells, enhancing the interpretation of cellular characteristics in H&E tissue sections.
Article 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.
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| http://dx.doi.org/10.1109/EMBC.2016.7590914 | DOI Listing |
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