Abstract
Motivation Although high-content image cytometry is becoming increasingly routine, processing the large amount of data acquired during time-lapse experiments remains a challenge. The majority of approaches for automated single-cell segmentation focus on flat, uniform fields of view covered with a single layer of cells. In the increasingly popular microfluidic devices that trap individual cells for long term imaging, these conditions are not met. Consequently, most segmentation techniques perform poorly. Incorporating information about the microfluidic features, media flow and morphology of the cells can substantially improve performance, though it may constrain the generalizability of software.
Results Here we present DISCO (Data Informed Segmentation of Cell Objects), a framework for using the physical constraints imposed by microfluidic traps, the shape based morphological constraints of budding yeast and temporal information about cell growth and motion, to allow tracking and segmentation of cells in micrflouidic devices. Using manually curated data sets, we demonstrate substantial improvements in both tracking and segmentation for this approach when compared with existing software.
Availability The MATLAB® code for the algorithm and for measuring performance is available at https://github.com/pswain/segmentation-software. The test images and the curated ground truth results used for comparing the algorithms are available at http://swainlab.bio.ed.ac.uk/.
