Nanolive’s label-free technology makes it possible to image cells for long periods of time, at high temporal resolution. The quantity and complexity of the images generated allows us to visualize biological processes in unprecedented detail, but also magnifies the challenges associated with image analysis. Manual image registration and analysis is impossible and so computer-aided processing must be used to harness data complexity. In this technical note, we introduce the key elements involved in cell segmentation, which are essential to understand the novelty of EVE Analytics (EA), Nanolive’s software solution for quantitative cell analysis. We then evaluate the performance of EA segmentation against fluorescence-based segmentation and compare how metrics produced by both approaches differ.