Abstract
Abstract
We introduce a large-scale single cell dataset for cell cycle phases. The dataset includes single cell images of 4′,6-diamidino-2-phenylindole-stained human epithelioma-2 (HEp-2) cells grown on microscopic glass slides. Greyscale images of adherent HEp-2 cells were acquired at 20x magnification by an automated, inverse microscope system. Cell images were automatically segmented with 256 × 256 pixel patch size including surrounding area. Initially, 10,000 images were manually classified by experts into 5 cell-cycle groups (interphase, prophase, metaphase, anaphase, telophase) and two additional groups (artefact, triple). Based on these initial single cell-image classifications, a convolutional neural network (CNN) model (VGG16) was trained. Subsequently, additional segmented single cell images were pre-classified by the CNN and double-blinded reviewed as well as corrected by two experts to build an algorithm-aided dataset with 100,000 images. The dataset provides a resource for development of biomedical and deep learning applications and, thus, enables training and assessment of cell-cycle detection algorithms.