Separation and isolation of living cells plays an important role in the fields of medicine and biology with label-free imaging often used for isolating cells. The analysis of label-free cell images has many challenges when examining the behavior of cells. This paper presents methods to analyze label-free cells. Many of the tools we describe are based on machine learning approaches. We also investigate ways of augmenting limited availability of training data. Our results demonstrate that our proposed methods are capable of successfully segmenting and classifying label-free cells.