Optimization of U-Net: convolutional networks for U87 human glioblastoma cell line segmentation

Official URL: https://dx.doi.org/10.1117/12.2595132

Glioblastoma multiforme (GBM) is one of the most aggressive primary brain tumors with its extreme proliferation and invasiveness. U87 human glioma cell line is one of the best representative cell lines for GBM with its extremely heterogenous and frequently altered morphologies. Quantification of heterogeneity and morphological changes of U87 glioma cells are mostly based on manual analysis. Therefore, automated image segmentation and analysis approaches are crucial. Here, we implemented U-Net algorithm for segmentation of U87 glioma cells and obtained 0.06% loss and 97.3% accuracy values. Next, we integrated Chan-Vese, K-means, and Morphological Filtering. Finally, we compared the performances of these approaches. We believe that our preliminary data might contribute to development of automated, reliable, accurate, and cell type specific image segmentation tools.