Comparison of Four Automatic Classifiers for Cancer Cell Phenotypes Using M-Phase Features Extracted from Brightfield Microscopy Images

In our in vitro study to model and understand the regulation networks that control the live and death of the cells, it is fundamental to quantify the contribution of each of the cancer cell’ phenotypes: apoptosis, cell cycle arrest, DNA damage repair, and DNA damage proliferation. For that, an automatic microscope is used to generate several images of cell populations using brightfield microscopy. In the scientific literature, several methods to extract features from microscopy images are available, but mostly for fluorescence or contrast phase microscopy, which have the disadvantage of being phototoxic to the cells, and therefore unsuitable for our study. In this paper a successful method to automatically extract and classify the phenotypes of cancer cells is presented. The method uses features extracted automatically from the M-phase (mitosis) of cells from images obtained by brightfield microscopy. The classification results are validated by comparing them with the correct manually annotated classes for each instance. Four different classifiers: Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), k-Nearest Neighbours (kNN), and Random Forests (RF) are compared using standard comparison metrics, such as precision, recall and F1-score. It is finally shown that the LDA classifier provided the best results, reaching an overall f1-score of 0.78 and an overall weighted f1-score of 0.88.