Journal: PLOS Computational Biology
Article Title: OmniSegger: A time-lapse image analysis pipeline for bacterial cells
doi: 10.1371/journal.pcbi.1013088
Figure Lengend Snippet: To characterize the performances of the competing packages, we first analyzed performance under the best case scenario: the proliferation of cells, with normal morphology, from a single cell to a monolayered microcolony under ideal imaging conditions. Panel A: Competing pipelines generate distinct cellular boundaries. The panel shows a phase contrast image of a microcolony. Competing cell segmentations are shown for a representative magnified region. All the pipelines, except CellProfiler (purple), lead to the same number of cells and are therefore acceptable for colony-scale analysis. The remaining pipelines generate significantly different cell boundaries at a sub-cellular resolution. Panel B: Lineage tree as generated by OmniSegger. Panel C: Quantitation of cell number. CellProfiler (purple) over-segments the cells to such a great extent that it roughly double counts cells. OmniSegger, SuperSegger, and DeLTA all show comparable performance. Panel D: Sub-cellular structure. Panel A visually illustrates the difference in the segmented cellular boundaries. To emphasize the biological significance of these differences, we generated histograms of cellular width measured by each pipeline and compared these to the true average cellular width (dotted line, inferred from cell contact). OmniSegger both generates a measurement with the smallest bias (1%) as well as the narrowest distribution ( σ / μ = 6%).
Article Snippet: SuperSegger [ ] , ✓ , ML-informed , Traditional , ✓ , mat , MATLAB , Linux,.
Techniques: Imaging, Generated, Quantitation Assay