ALFI: Cell cycle phenotype annotations of label-free time-lapse imaging data from cultured human cells

Detecting and tracking multiple moving objects in a video is a challenging task. For living cells, thetask becomes even more arduous as cells change their morphology over time, can partially overlap,and mitosis leads to new cells. Differently from fluorescence microscopy, label-free techniques can beeasily applied to almost all cell lines, reducing sample preparation complexity and phototoxicity. In thisstudy, we present ALFI, a dataset of images and annotations for label-free microscopy, made publiclyavailable to the scientific community, that notably extends the current panorama of expertly labeleddata for detection and tracking of cultured living nontransformed and cancer human cells. It consists of29 time-lapse image sequences from HeLa, U2OS, and hTERT RPE-1 cells under different experimentalconditions, acquired by differential interference contrast microscopy, for a total of 237.9 hours. Itcontains various annotations (pixel-wise segmentation masks, object-wise bounding boxes, trackinginformation). The dataset is useful for testing and comparing methods for identifying interphase andmitotic events and reconstructing their lineage, and for discriminating different cellular phenotypes.