Robust Cell Segmentation for Histological Images of Glioblastoma.

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.