Deep morphological quantification and clustering of brain cancer cells using phase-contrast imaging

Sammanfattning: Glioblastoma Multiforme (GBM) is a very aggressive brain tumour. Previous studies have suggested that the morphological distribution of single GBM cells may hold information about the severity. This study aims to find if there is a potential for automated morphological qualification and clustering of GBM cells and what it shows. In this context, phase-contrast images from 10 different GBMcell cultures were analyzed. To test the hypothesis that morphological differences exist between the cell cultures, images of single GBM cells images were created from an image over the well using CellProfiler and Python. Singlecellimages were passed through multiple different feature extraction models to identify the model showing the most promise for this dataset. The features were then clustered and quantified to see if any differentiation exists between the cell cultures. The results suggest morphological feature differences exist between GBM cell cultures when using automated models. The siamese network managed to construct clusters of cells having very similar morphology. I conclude that the 10 cell cultures seem to have cells with morphological differences. This highlights the importance of future studies to find what these morphological differences imply for the patients' survivability and choice of treatment.