About this item:

195 Views | 734 Downloads


Research Funding:

This research is supported by grants from National Institute of Health K25CA181503 and R01CA176659, National Science Foundation ACI 1443054 and IIS 1350885, and CNPq.


  • Science & Technology
  • Technology
  • Life Sciences & Biomedicine
  • Engineering, Biomedical
  • Radiology, Nuclear Medicine & Medical Imaging
  • Engineering
  • Histological Image
  • seed detection
  • cell segmentation
  • Hessian
  • iterative merging

Robust Cell Segmentation For Histological Images Of Glioblastoma


Proceedings Title:

2016 Ieee 13th International Symposium On Biomedical Imaging (Isbi)

Conference Name:

IEEE 13th International Symposium on Biomedical Imaging (ISBI)


Conference Place:



Volume 2016

Publication Date:

Type of Work:

Conference | Post-print: After Peer Review


Glioblastoma (GBM) is a malignant brain tumor with uniformly dismal prognosis. Quantitative analysis of GBM cells is an important avenue to extract latent histologic disease signatures to correlate with molecular underpinnings and clinical outcomes. As a prerequisite, a robust and accurate cell segmentation is required. In this paper, we present an automated cell segmentation method that can satisfactorily address segmentation of overlapped cells commonly seen in GBM histology specimens. This method first detects cells with seed connectivity, distance constraints, image edge map, and a shape-based voting image. Initialized by identified seeds, cell boundaries are deformed with an improved variational level set method that can handle clumped cells. We test our method on 40 histological images of GBM with human annotations. The validation results suggest that our cell segmentation method is promising and represents an advance in quantitative cancer research.

Copyright information:

© 2016, IEEE

Export to EndNote