Segmenting Neuroblastoma Tumor Images and Splitting Overlapping Cells Using Shortest Paths between Cell Contour Convex Regions
Neuroblastoma is one of the most fatal paediatric cancers. One of the major prognostic factors for neuroblastoma tumour is the total number of neuroblastic cells. In this paper, we develop a fully automated system for counting the total number of neuroblastic cells within the images derived from Hematoxylin and Eosin stained histological slides by considering the overlapping cells. We finally propose a novel multi-stage cell counting algorithm, in which cellular regions are extracted using an adaptive thresholding technique. Overlapping and single cells are discriminated using morphological differences. We propose a novel cell splitting algorithm to split overlapping cells into single cells using the shortest path between contours of convex regions.
KeywordsHistological image segmentation splitting overlapping cells neuroblastoma
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- 2.Fatakdawala, H., Xu, J., Basavanhally, A., Bhanot, G., Ganesan, S., Feldman, M., Tomaszewski, J., Madabhushi, A.: Expectation–maximization-driven geodesic active contour with overlap resolution (emagacor): Application to lymphocyte segmentation on breast cancer histopathology. IEEE Transactions on Biomedical Engineering 57(7), 1676–1689 (2010)CrossRefGoogle Scholar
- 6.Powers, D.: Evaluation: From precision, recall and f-factor to roc, informedness, markedness & correlation, School of Informatics and Engineering, Flinders University, Adelaide, Australia, Tech. Rep. SIE-07-001Google Scholar
- 8.Haralick, R., Sternberg, S., Zhuang, X.: Image analysis using mathematical morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence (4), 532–550 (1987)Google Scholar