Synthetic training of machine learning algorithms for holographic cell imaging

In this work we seek to demonstrate acceptable classification performance for classifiers trained using augmented training data of synthetic cells imaged by simulated holography and evaluated using experimentally collected holographic data. In particular, we utilize experimentally collected DHM phase maps derived from the MDA-MB-231 breast cancer cell and immortalized human gingival fibroblast (GIE) cell lines. Automated segmentation was performed using a floodfill clustering approach and experimental feature distributions were used to generate statistically random synthetic cell realizations for each class.