Cell lineage tracing in lens-free microscopy videos

Abstract

In vitro experiments with cell cultures are essential for studying growth and migration behaviour and thus, for gaining a better understanding of cancer progression and its treatment. While recent progress in lens-free microscopy (LFM) has rendered it an inexpensive tool for continuous monitoring of these experiments, there is only little work on analysing such time-lapse sequences. We propose (1) a cell detector for LFM images based on residual learning, and (2) a probabilistic model based on moral lineage tracing that explicitly handles multiple detections and temporal successor hypotheses by clustering and tracking simultaneously. (3) We benchmark our method on several hours of LFM time-lapse sequences in terms of detection and tracking scores. Finally, (4) we demonstrate its effectiveness for quantifying cell population dynamics.