Multireference level set for the characterization of nuclear morphology in glioblastoma multiforme

Hang Chang, Ju Han, Paul T. Spellman, Bahram Parvin

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Histological tissue sections provide rich information and continue to be the gold standard for the assessment of tissue neoplasm. However, there are a significant amount of technical and biological variations that impede analysis of large histological datasets. In this paper, we have proposed a novel approach for nuclear segmentation in tumor histology sections, which addresses the problem of technical and biological variations by incorporating information from both manually annotated reference patches and the original image. Subsequently, the solution is formulated within a multireference level set framework. This approach has been validated on manually annotated samples and then applied to the TCGA glioblastoma multiforme (GBM) dataset consisting of 440 whole mount tissue sections scanned with either a 20× or 40 × objective, in which, each tissue section varies in size from 40k × 40k pixels to 100k × 100k pixels. Experimental results show a superior performance of the proposed method in comparison with present state of art techniques.

Original languageEnglish (US)
Article number6298001
Pages (from-to)3460-3467
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Issue number12
StatePublished - 2012


  • Multireference level set
  • nuclear segmentation
  • tumor histology sections

ASJC Scopus subject areas

  • Biomedical Engineering


Dive into the research topics of 'Multireference level set for the characterization of nuclear morphology in glioblastoma multiforme'. Together they form a unique fingerprint.

Cite this